Now updated, here are all four new videos produced by the “I, Too, Am Auckland” team. This iteration’s theme is titled, “CONVERSATIONS.” We hope the videos illustrate how critical dialogue can help us reflect on our own biases and stimulate change at both the interpersonal and institutional levels when addressing everyday colonialism and racism. Enjoy…

“Sam & Sehar”

“Caitlin & Emmy”

“Shameela & Atelaite”

“Lydia & Barek”

firstcontactI am always on the lookout for some new teaching ideas (I teach a 5/5 load, people, you have to find new things to do if you are not going to insane). So, I got First Contact – Teaching and Learning in Introductory Sociology, hoping it would contain a lot of ideas about teach intro (something I teach A LOT). The book was also reviewed in the July 2014 of Teaching Sociology (which is where I saw it mentioned). So, I decided to read the book before reading the review.

I have to say that this book turned out to be a major disappointment. The only way anyone can find this book useful is if they are completely new at teaching, as in, no teaching experience whatsoever, or completely clueless about this whole teaching business. So, if you are in that position, starting to teach from scratch, and this is your first introduction class ever, then, you might find this book helpful.

So, it may very well be that I have been teaching for a long time (I taught my first class in Spring 1997, language in society, as a graduate student, to linguistics major, at the University of Nice, in France). But I think that no matter how long one has been teaching, there is always room for improvement. And frankly, teaching has changed dramatically in the past 17 years of my teaching career. Technology has dramatically altered how we do things. Online education (or “education”, if one wants to be cranky about it) and hybrids have exploded into the field of digital learning. So, this isn’t your grandfather’s introduction to sociology anymore.

The interesting thing is that the basic building blocks of introductory sociology courses has not changed from where I started to teach in the United States in 2000. You just need to look at the table of content for any sociology text and go back 15 years, you won’t find much change in the way we teach introduction to sociology. So, any changes or innovation have to come from somewhere else. I was hoping the book would address the “somewhere else”.

I was also hoping to get some ideas about the perennial struggle of the sociology instructor: fight the psychology bias of American students, along with commonsense, and half-baked economic ideas.

While the book acknowledges all of these challenges (changes in teaching with increased focus on learning, the persistence of how we teach introduction to sociology, and the individualistic bias of our audience), it never really addresses them. And that is the main problem with this book: it remain much too general to be of use. The book painstakingly goes over every minute components of the syllabus but this is the wrong focus and that is not useful because this is information that is either largely provided by one’s institution, and it is not hard to find a generic template. One does not need a book for that.

The second major issue, to me, was that the book is not enough about sociology. A lot of what is mentioned, whether it’s about assessment or student engagement, could apply to any other discipline. Most of the time, the book reads like a compendium on best practices in teaching rather than specifically about teaching introduction to sociology.

The specific challenges of teaching sociology get only superficial treatment. When it comes to selecting course materials or discussing sociology directly, or reviewing the literature on teaching sociology, some of the references used date from the 80s or 90s. Sorry, but that does not cut it and it does not help dealing with contemporary issues in teaching introduction to sociology. Part of the frustration was that the book never really takes a stance on anything, whether it is on textbook and material options, or anything else. It lays out the issues but never really deals with them or takes a position.

So, again, if you are brand new to teaching, then, maybe, you’ll find this book useful and helpful. But if you have the slightest bit of experience, then, frankly, it will be waste of your time. Which is a shame because there is a need for a book on this topic, but this one is not it.

We all love to teach statistics to undergraduate students, don’t we? Of course we do. </snark>

We know teaching stats is not easy and students hate maths (and some of us do too). And yet, it is a rite of passage and we all have to get through it. Well, The Cartoon Introduction to Statistics by Grady Klein and Alan Dabney might help. As the title helpfully notes, this is a cartoon book. It is also a very basic introduction to statistical concepts and ideas. May I emphasize: very basic.

And let me emphasize something else: no maths.

That’s right. There is a little appendix at the end with a few formulas but nothing much really. The whole idea is to focus on concepts, not technicalities and maths. In other words, this book is not a substitute for a regular statistical textbook. But it might make digesting the maths a bit easier. This book might be a nice addition to existing course materials if you are looking for something a little lighthearted and humorous.

I should also add that this book does not cover the entirety of the usual curriculum and topics that you would find in a regular undergraduate statistical course.

The book tries to convey a sense of how pervasive and useful statistics are in daily life. It uses concrete examples, again, with some humor. It does cover descriptive statistics, measures of central tendencies, normal distribution, the central limit theorem, a little bit of probabilities (but really, not a whole lot), inference and hypothesis testing, confidence levels and intervals. Again, with no maths.

There is a single idea that drives the entire book (and one that makes it, at times, a bit repetitive): one can really never know about the characteristics of an entire population, but we can know some things about parts of that population, through statistics. That is the main thesis. However, we can never be 100% sure of the information we get through statistics, because statistics do not measure entire populations, just little chunks of it, that is, samples. This is the theme of the book and this gets repeated in almost every chapter.

I would think that undergraduate students would find such a book attractive and fun to get through. The fictional examples used are indeed pretty fun (dragons, vikings, monsters, aliens, and Crazy Billy’s Bait Barn). Again, this will not substitute for textbooks, real maths, and real statistics professors, but this might make a nice (and relatively cheap) addition to any course.

Now, the cartooning… After all, this is a cartoon introduction. If you follow this blog, you know that we have a gallery of sociologists cartooned by Kevin Moore. I was not thrilled about the cartooning in the book. It might be partly because Kevin Moore has completely spoiled me because his cartooning is so great. The cartooning in Klein and Dabney’s book was, I think, a bit “fuzzy”. I tend to think clear-cut things and the cartooning felt unfinished and a bit sloppy. It was not the grey scale. I was ok with that and full colors might have actually made the whole thing look too busy. I just wish the drawing had been clearer and neater.

But, again, this might be worth recommending to students who are a bit worried about having to take a statistics class. More than that, I think there is a lot of room for more cartooning introduction to sociology-related topics.

Haven’t you heard that answer when you asked students questions such as “why is the homicide rate higher in the US than in other high-income countries?” or other some such questions? And you push forth explaining rates and ratios and all these things, trying to be as convincing as possible… until the next question comes up and you get the same answer: it’s because there are more people. It’s as if there is some automatic belief that a larger population will automatically cause more of something (whatever it is).

And here comes along Danny Dorling with the same puzzle and a brutal but good answer, from his latest book, Population 10 Billion:

“It was at this symposium that the Ugandan Minister of Finance and Planning, the Honourable Professor Ephraim Kamuntu, felt he needed to point out to the audience that ‘. . . the developing world contributes the least greenhouse gas emissions, that they will be most affected by climate change, and that they are least able to deal with the negative effects’. He was then, in effect, rebuked by the keynote speaker, Jonathan Porritt, son of the former colonial governor of New Zealand, who ‘. . . reminded the audience that we need to get beyond the “crass” consumption versus population debate’. 27 But Kamuntu was right and Porritt was wrong. What is crass about explaining that it is consumption, not population, that matters, and why does Porritt either not appear to understand that, or not want us to understand it? Does he want a world with fewer people but where a minority can still consume very highly, in place of the thousands who don’t exist?

Suggesting that consumption and population both matter is identical to suggesting that when it comes to murder, both violence and population matter. The higher a level of violence you have, the more murders you get, and simultaneously, the more people you have, the more murders you get, as there are more people available to murder. This is simply stupid. Murder rates fall in countries where levels of violence fall, even as population rises. Our rate of murder, if the number of holes in ancient human skulls is any indication, was highest in our distant past. Most of us have never been as peaceful as we are today.


Proponents of population scare stories say that as every extra human must consume something, this argument does not apply to consumption. You cannot have a negative consumption rate for a person. However, the same is true of murder. You cannot have a negative murder rate for a group, but some extra people can help others to murder less, just as some extra people can teach others to consume less and hence reduce consumption overall, even as population rises.” (Loc. 1767-1789)

In the soon-to-no-longer-be-used Microcase workbook I have been using for years, one of the exercises, in the chapter on socialization, involved looking at what particular traits people think children should possess, in different countries. The data for this exercise came from the World Value Survey. In the WVS, you can only download full datasets, formatted for SPSS, SAS, or STATA, which I don’t have at home. However, the website offers a neat analysis tool where you can conduct some analysis in your browser, selecting the variables and countries you want, and then, download the result in Excel. Thanks to that and Tableau, I was able to reconstruct the exercise.

Results below: bar charts showing what percentage of surveyed people, in selected countries (missing a lot of data from Africa, as usual), think children should have the following traits:

Determination / perseverance

In this case, I think it is more interesting to look at which countries do not really value that trait all that much, that is, the bottom of the list, rather than the top. And what’s with Switzerland?


Note how the percentages go way up compared to the previous one, where the maximum value was 72.5%. Note also the strong showing of Asian countries toward the top.

Hard work

Note the absence of Western countries from the top and their stronger presence at the bottom. I blame Montessori education and pop psychology, and also, affluence.


And here, Western, wealthy, countries make a strong showing at the top, not very surprisingly. But note how low the percentages are, even for the top, and how really low they are at the bottom.


This one leads to more mixed results and not particular geographical trends.


Here again, it is not surprising to find no Western countries at the top, but poorer, and, one can assume, more traditionalist countries where obedience is more valued. It is interesting to find Japan and Hong Kong way at the bottom, with very low rates.

Tolerance and respect

Here again, we find Western countries at the top, considering tolerance and respect are fairly liberal values.

Religious faith

Countries with strong Muslim populations take the first three slots. After that, the rates go down pretty quickly. Why is Hong Kong always at the bottom?


Asian countries occupy the top on that one (except Hong King, again, at the bottom). Wealthier countries, overall, don’t seem to care all that much.


The top percentages for this one are not all that high to start with. Very quickly, the percentages get under 50%. Why would that be?

[In order to get a better view of larger visualizations, you should click on the “<>” symbols on the upper right corner of the page for flexible page width. I have finally figured out how to embed from Tableau but it makes a mess of the page. As always, click on the images for larger, interactive, views]

In this post, I will wrap up, for now, another set of visualizations on global opinions on homosexuality that can be used as sociological exercises in data analysis. Again, the data come from the Pew Research Center and the visualizations were made in Tableau.

Quite often, one hears the argument that views on homosexuality are generational: younger people are more tolerant than older generations. So let’s explore that hypothesis with global data. For that purpose, I thought it might be useful to divide the set of countries into geographical regions, and then, get the average:

Regional Averages

Quite clearly, in all regions except Africa (for which the rates of acceptance of homosexuality are very low across the board), the hypothesis is supported. Older categories seem to be less accepting of homosexuality.

Let us now go region by region and look at selected countries for each.



Right away, you can see that the average for Africa would be even lower if it weren’t for South Africa. For South Africa, the rates much higher than for the rest of the region, but they do fit the pattern of greater acceptance of homosexuality for younger people. Otherwise, it is hard to distinguish a clear pattern for the other countries as the rates are really low. Look at Uganda, for instance. It is the opposite of what one would expect. And even though there is one age category for which data is missing in Kenya, the pattern is reversed. But again, with such low rates, little differences look like larger differences.

Let’s look at the other low average region: the Middle East:

Middle East

Here, it is Israel that is the big outlier for the whole region and drives up the average, as South Africa did for Africa. And for Israel alone, one can see that the middle age cohort is the one with the highest acceptance rate. Lebanon then follows, with a pattern supporting our original hypothesis. Then Turkey, with rates much lower than Israel and Lebanon, but higher than the rest of the region, and this time, it is the middle age cohort that is the least accepting (but again, the actual percentage point differences are very low). I confess to being surprised by the overall lack of acceptance in Tunisia. I guess secularism does not extend to attitudes regarding homosexuality.

Next up, Asia:


This is one of these cases where the average is actually misleading (see back up) especially when the countries are so divided. On the one hand, you have countries with very high rates of acceptance (Australian, Japan, Philippines, and to some extent, South Korea… look up South Korea in my previous post, it was interesting case there). And one the other hand, countries with very low acceptance rates (China, Indonesia, Malaysia, and Pakistan). But an average smooths these massive differences out. That is why looking country by country is necessary. If I were to hypothesize, I would argue that the high acceptance countries are either Christian or more secular compared to Muslim, more religious countries.

Does our generational pattern hold here? Mostly yes. We lost the patterns only for the countries for extremely low acceptance rates.

Moving on, Central / South America:

South America

Obviously, the rates are high and our age pattern holds solidly for every country in our sample. El Salvador and Bolivia seem to be trailing behind a bit. That is usually an indication that some more digging is required, especially some correlation work. On the other hand, Argentina, Chile, and Brazil have very high rates. Venezuela and Mexico play middle of the pack. Those high rates are interesting in a region marked by strong Catholicism, but also Pentecostalism.

Let’s move North and look at North America:

North America

Depending on how you look at it, either Canada is driving up the regional average, or the US is driving it down. I blame evangelicalism, puritanism and conservatism. The US rates are actually comparable to the middle of the pack South American countries and other countries on other regions score higher. This validates the idea that economically, the US is a highly developed, core country, but on social issues and indicators, it scores in a fashion resembling more semi-peripheral countries. Our age hypothesis, though, holds for both countries.

And last but not least, Europe:


Obviously, for Western and Northern Europe, the rates are incredibly high. However, no one following the news should be surprised by the low rates in Russia, Poland, and Greece.

For instance, this:

Russia‘s president, Vladimir Putin, has signed into law a measure that stigmatises gay people and bans giving children any information about homosexuality.

The lower house of Russia’s parliament unanimously passed the Kremlin-backed bill on 11 June and the upper house approved it last week.

The Kremlin announced on Sunday that Putin had signed the legislation into law.

The ban on “propaganda of nontraditional sexual relations” is part of an effort to promote traditional Russian values over western liberalism, which the Kremlin and the Russian orthodox church see as corrupting Russian youth and contributing to the protests against Putin’s rule.

Hefty fines can now be imposed on those who provide information about the lesbian, gay, bisexual and transgender community to minors or hold gay pride rallies.”

So, no surprise there. Things are chaotic in Greece with the rise of neo-fascists (who are usually not friendly to gay even though these movements drip homo-eroticism).

The age pattern, though is much more irregular, but within the context of high rates across the board for the other countries.

Finally, and just for fun, I tried my hand at a heat map on this. The colors correspond to the regions (and the countries are grouped that way). The size of the square is a function of the %, by age categories.

Heat map

That is it on this topic. As you can see, there is a lot of exploration to be done and puzzles to be teased out on this.

[Note: for all the visualizations, you can get larger views by clicking on the images.]

I have never found culture to be a particularly difficult or painful topic to teach in introduction to sociology classes. There is though a tendency in my students to perceive the relationships between technology and culture to be a one way street: technology -> culture and behavior. One of the challenges is to explore the other way around in that relationship: culture -> technology -> behavior or culture -> behavior -> technology. And that is even before you start discussing the social structures of technological production and the social institutions involved in the process (education, government, military, etc.). And that is before you even begin to discuss the fact that technology access and use is never homogeneously distributed within and between population (e.g. the digital divide), and that layers of social stratification are crucial in that respect.

But anyhoo, it is always interesting to trying to explore some data on technology as part of a larger discussion on other non-material aspects of culture, as they are all interrelated. This is my attempt at doing so (I’ll deal with values and norms in another post). I got all the data from Gapminder. The maps and bar charts were done in Tableau (sorry I still cannot embed my workbooks here, so, links to Tableau Public will have to do). The Gapminder screengrabs are what I did when I combined technology variables with GDP per capita (as a rough measure of economic development). Click on the screen grabs to go to the Gapminder interactive graphs. At the same time, because the technologies I an discussing below are relatively recent, the time series are not as crucial as they might be for other variables.

Anyhoo, feel free to use these visualizations as part of exercises to get students to think about data, patterns and technology.

So, one easy way to start is with personal computers.

Personal computer per 100 people (interactive world map and rankings via Tableau)

Static map:

PC per 100 map

It is not hard to see the big gap between North America / Western Europe and pretty much everybody else. This is more visible in the rankings.

Static partial rankings:

PC per 100 - Partial rankings

Personal computer per 100 people correlated with GDP per capital (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

PC per person and GDP

I would think that 2006 data for PCs are already pretty old data, but that was the most recent on Gapminder, so there you have it. As you can see though, coloring the bubbles based on GDP per capita helps identify a pattern of higher income -> higher availability of technology, but with a very steep curve (therefore, a bigger gap between high PC users / high GDP per capita versus the rest). One interesting question is whether this clear pattern will persist across other related technologies.

Like cell phones:

Cell phone rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Mobiles per 100 people - Map

The rates are markedly different than those for PCs (and the data a bit more recent). Anyone who travels to the Global South has to notice the omnipresence of mobile phones. It looks like a lot of countries of the Global South practically bypassed the landline stage and went straight to mobile. The rates reflect that, even though stratification patterns are still obvious.

Partial rankings – static image:

Mobiles per 100 people - Partial rankings

Cell phone rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Cell phones oer 100 and GDP

As you can see, this is a very different bubble chart than the preceding one. No steep, highly stratified curve here. There is definitely a positive correlation between GDP per capita and mobile phones subscriptions, as reflected by the color gradient, but the curve is more regular and linear.

Internet use rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Internet per 100 map

Regional patterns are rather clear on this one. Of course, the data only measure internet use, but not the quality of connections and networks.

Static partial rankings:

Internet per 100 - Partial rankings

One can clearly in which countries full access is available and achieved.

Internet user rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Internet per 100 and GDP

Here, we get a third different pattern: steep and linear, with the same stratification.

So, we know that Internet use is pretty widespread, except for the poorest countries in Sub-Saharan Africa, but as I mentioned above, Internet use does not measure quality of connection. So, let’s look at broadband.

Broadband rates per 100 people (interactive world map and rankings via Tableau)

Static image for map:

Broadband per 100 people - Map

Static partial rankings:

Broadband per 100 - partial rankings

The rates are overall much lower, across the board (with two top outliers), so one can see that broadband penetration still has a way to go, even in the richer countries.

Broadband rates per 100 correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Broadband per 100 and GDP

And here is a fourth different pattern: a lot of countries at the bottom of the graph, close to 0, with a very flat pattern, a slow rise as we get towards the high GDP per capita countries, a very smooth and slow upward curve (with our two outliers in the top right corner).

So, again, even though the different technologies I have visualized here are related to each other, one can see that their access varies widely, by technology, by regions, and by levels of development. At the same time, comparative graphs also show different patterns of global penetration of these technology, from brutally stratified curves (PC), to almost perfectly linear (mobile subscriptions), to steep curve (Internet), to long left-tailed curve (broadband). The trick would be to try to find explanations for these different patterns.

And, of course, we focused only on the technologies themselves here. Not on what people do with them and the different forms of usage. That is for another post.

Ok, so yesterday, I went over a possible exercise I had in mind for a module on data analysis for my introduction class. Today, I thought I might as well share the databases that I have started collecting for the purpose of designing more such exercises on different topics. So, here is a partial list that I will constantly update as I find good stuff on the Intertoobz.

International Organizations

Other Global Data

US Data

There are others, I’m sure. And I’ll add them as I find them.

Also, some of these databases contain tools to create customized visualizations or to manipulate the data, like the Human Development Report.

Otherwise, there are a couple of tools I’m seriously considering:

  • GSS (via Survey Documentation and Analysis, Berkeley… thanks to Jay Livingstone for mentioning this to me)
  • Statwing (I’m still on the fence on this one because even though it looks like a neat statistical software that is actually affordable, the visualization part lacks options to customize and you might end up having to download things back to Excel, which I’d rather avoid. I wish the software did the whole “upload / visualize / analyze / publish” thing. But I need to work more on this)
  • Health Data Interactive

As much as I can, I only integrate a data analysis component to my introduction to sociology classes. I am not trying to do anything really complicated but I want my students to get a very basic taste of what it means to think with data. For a long time, I had the perfect tool at hand in the form of Microcase Workbooks. There were several of them (a couple for introduction, one for marriages and families, one for social research). MicroCase is a bare bone version of more commons statist8ical software in the social sciences. It is a small program (does not take too much space on your hard drive) that runs on Windows only. However, it uses the GSS, American Community Survey, the World Value Survey and gives students the opportunity to select their own variables, construct their own tables / maps / pie charts / scatterplots / time lines. Students have always found it easy to use and actually fun. Well, that is over as the publisher decided to no longer update the software or the databases. Since then, I have been looking for alternatives. And, of course, publishers’ reps have been more than eager to try to sell me on their latest tools… which are all inadequate for my own purpose. And sending intro students into SPSS is out of the question… heck, I don’t want to go into SPSS.

So, what is a SocProf supposed to do? Well, there are now tons of data and databases that are publicly available. Why not create my own exercises? It will be cheaper to my students and my exercises can be exactly the way I want them. There are also now a lot of visualizing tools, either directly provided by the same organizations that make the data available (like the UN development report or Gapminder). I don’t get dependent upon the good will of a corporate publisher to keep on updating a product that is going to be costly to students. Win-win. On the losing side, it is going to be time-consuming to build up these exercises. I just spent an hour cleaning up data from the CDC on suicide in the US. And it the visualization tools are not available, I can always use Tableau.

So, for instance, indeed, I started simple with some data on suicide in the US. The CDC was the organization with the most data on that. Starting with this:

Suicide Map 1

The first problem with this map is that it is not interactive and the level of detail (by county) makes it a bit busy even if you can clearly regional patterns. These regional patterns actually make for an interesting puzzle for my students to solve. That can be a starting point but it is hard to create rankings, for instance.

A second option is to use the CDC interactive tool through WISQARS. So, basically, it looks like this:

Suicide CDC Interactive 1

As you can see above, you have a series of menus, drop down and radio buttons. You can filter things out. I kept the entire US but I selected “suicide” for intent of injury. And I kept the largest spread (2000 – 2006). I kept all the demographic subset at default. And I  got this as a result:

Suicide CDC Interactive 2

Several problems, with this: (1) on the right hand side, it says “Hover over a state with your mouse to see its name and rate”… that does not work. I tried different browsers including *gasp* Explorer, and no dice. (2) The export data function creates a csv file that takes a lot of cleaning up if you want to do the most simple statistical operations and visualizations. Which is what I ultimately ended up doing in Tableau Public (sorry, the embed still does not work).

The map, though, shows the same pattern as the county one above.

Third option, if you really want an interactive map, and still from the CDC, there is another interactive tool that is a bit trickier to manipulate but does the job: Health Data Interactive:

Suicide CDC Interactive 3

Again, you get to set your options and get an interactive map (with some missing data and only 44 states reporting, which is kinda annoying).

Beyond maps, though, the CDC has some good data visualizations but again, the raw data are harder to track down. For instance, you can get a broad overview over time:

Suicide Overall

Again, you can set up some interesting questions regarding the shifts in age categories with the highest suicide rates, when the shift happened and why. But you can drill down even further and consider race and ethnicity:

Suicide Race Ethnicity

Why whites and American Indian / Alaskan Native / Pacific Islanders (from my little Tableau thing, we already know that Alaska has a high rate)?

Ok, let’s add sex into the mix:

Suicide Race Ethnicity Sex

Across the board, men are way more likely to commit suicide than women. Adding sex does not alter the racial / ethnic patterns. So, should we pity white men after all?’

Finally, let’s add age. Let’s start with the 10-24 age category:

Suicide Age 10-24

One can only ask, what is going on with young American Indian / Alaskan Native / Pacific Islanders? Whites are no longer strikingly higher than other racial and ethnic category, for that age category.

But once you move up the age ladder, into the 25 – 64 category:

Suicide Age 25-64

Then, whites pop up again in the higher rates.

Ok, how about 65 and older:

Suicide Age 65 over

See what happens with American Indian / Alaskan Native / Pacific Islanders? And Whites?

Ok, how about some trends?

Suicide gender trend

Note the uptick with the recession. Otherwise, a familiar gender pattern.

Let’s separate men and women and compare by age categories, first, for men:

Suicide trend males age

The interesting trend here is the progressive joining of the 25-64 (up) and the 65+ (down).

Now, women:

Suicide trend females age

Now, we already know that women are much less likely to commit suicide than men. And this visualization has an extra age category but one can see that the relative increase is greater for women than men. This is especially the case in the 45-54 category.

And now, for the fun of a different visualization, let’s add yet another variable: the means of suicide:

Suicide mechanisms

I am normally not a big fan of stacked bars, but in this case, I think it works. You can clearly see that men are more likely to use a firearm in all age categories whereas suffocation and poisoning are more used by women. One could explore access and cultural factors in the decision to use one mechanisms or another to kill oneself.

This gender aspect is more visible if one filters out other variables:

Suicide mechanisms gender

So, as you can see, there is a lot to explore and a lot of sociological puzzles to be solved, just by using some very basic data, with limited variables, and just by using publicly available data visualizations.

I’ll continue to share these things as I build them.

By David Mayeda

In the 1960s and ’70s, labeling theory (a.k.a., social reaction theory) gained criminological prominence. Demonstrating a shift towards the critical criminology school of thought, labeling theory questions the broader power structure by asking two overarching questions:

  1. How do those with substantial power in society label people with less power and their behaviors deviant?
  2. What effects do those labels have on the future lives and behaviors of the people being labeled?

Labeling theorists, such as Edward Lemert, note that almost all people engage in primary deviance – petty crimes (e.g., truancy, petty theft) during their youth. This is normal. However, when people engage in these types of behavior and get caught, sometimes the social reaction is overly punitive. When this happens, the normalized behavior is redefined as criminogenic behavior, and the individual’s identity transforms from normal, everyday kid to “screw up,” “problem child,” “criminal,” etc.

As the self-fulfilling prophecy manifests, the individual becomes ostracized from conventional peers and adults, and finds comfort engulfed by similarly defined peers, all leading to engagement in secondary deviance, where the individual’s roles and identities revolve increasingly around criminalized behaviors. In turn, the individual’s deviant master status is further cemented.

Labeling theorists also suggested a deviant master status gets cemented as one goes further through the criminal (or juvenile) justice system, from arrest to conviction to incarceration, and that degradation ceremonies in formal, state justice systems are highly effective in cementing the criminally stigmatized master status.

Also of critical importance, Howard Becker argued that moral crusaders were those with conventional power who thrust their values upon society by stigmatizing minority groups. Working in concert, moral entrepreneurs included those who would utilize propaganda purported by moral crusaders in order to profit financially through minority groups’ stigmatization.

This aspect of labeling theory is important to remember because it is those with power who create society’s rules and laws, and use the law to protect their privilege. Hence, moral crusaders and entrepreneurs have the social capital – the money, the connections, the clout – to work with media, businesses, and politicians in suppressing any contestation to the status quo by labeling threats as deviant.

Of course minorities are not only labeled deviant as criminals. Additionally, they can be stigmatized through labels tied to mental illness. Such labels rely on the medical field’s social prestige, and focus on individuals’ alleged mental health problems (e.g., inability to focus, propensity to resist authority, substance use concerns), thereby detracting attention away from broader social inequities that ultimately cause disproportionately high levels of mental health concerns in minority communities.

And now onto The Wire

In this series of clips from season four, we see “Major Colvin” (or “Bunny,” now retired from the Baltimore police force) working with a university professor and his graduate students. The team is running an experimental alternative middle school class for students who have not adjusted well to mainstream courses, which includes main character “Namond.” The alternative course’s developers feel by removing disruptive students, the mainstream courses can function more smoothly, while the sequestered students can receive more attention. Still in the class’s early stages, Namond, does not trust the situation he has been forced into:

Notice how at 0:45 of this video, graduate assistant, “Miss Mason,” labels students with a variety of mental health conditions. In doing so, concerns are individualized, disconnected from the poverty that encapsulates the students’ proximal surroundings, as well as from the extensive social stratification that characterizes Baltimore as a whole.

And at the end of the video as Namond challenges the class leadership, notice how he embraces his identity as a “troubled youth,” talking back to the teachers and offering his hands so he can be cuffed. He fulfills the prophecy tagged upon him, while engulfed by similarly defined students.

Now fast-forward to a point when this class has matured a bit. Most of the students have developed a better rapport with the teachers, but still question the value that their educational system offers:

Here the “corner boys” (and girls) educate the teachers on the ins and outs of slingin’ drugs. At 2:00, see how Major Colvin likens the education system to any other system that teaches youth to manipulate their surroundings, to “practice getting over, try runnin’ all different kind of games. You know it’s practice for the corner (where drugs are sold), right?”

Perhaps Major Colvin is critiquing the youth and their efforts in the mainstream education system. However, the youth go on to explain how the capitalist system works in their neighborhood, with the panopticon persistently present; someone with higher authority is always watching the subordinate workers to ensure management is not cheated. Here, we see the labeled youth, segregated from their peers demonstrate their skillsets, which have been ignored by the mainstream system.

And at 4:42, Namond returns to drive home labeling theory’s key dimension. Although these youth of color are labeled animals, larger institutions in society – Enron Corporation, government, alcohol and cigarette industries, sports – also cheat, and do so in much more profound ways as society’s real killers. “D” straight up asks, “And drugs, pays your salaries, right?,” revealing that Major Colvin and his colleagues may inadvertently be moral entrepreneurs who profit through governmental funding to run programs for youth that have been labeled “troubled.”

Namond begins to sum it up: “We do the same thing as you all. Except when we do it, it’s like, ‘Oh my God, these kids is animals,’ like it’s the end of the world comin’. Man that’s bullshit… Hypocritical.” Zinobia closes out, “I mean yeah we got our thing but, it’s just part of the big thing.”

Yup, but in accordance with labeling theory, those with widespread power who truly profit by exploiting others through the big thing (i.e., capitalism) are labeled innovative businessmen, not animals.

By David Mayeda

For Emile Durkheim, anomie was a state of normlessness, a society where individuals’ connections with each other had become frayed. This happened during times of massive social change and could lead to heavier patterns of suicide. For Durkheim, the other critical aspect of anomie was that it existed when there was an absence in social regulations that would help to guide behaviours. Or put in more Durkheim-esque terms, anomie equates to normlessness in social regulations.

That is partly what we see in this clip from HBO’s awesome drama, The Wire (season 3). Here Major Colvin (a.k.a., “Bunny”, pictured below) has established a safe zone of sorts for mid-level drug dealers from a variety of gangs. This sector becomes called “Hamsterdam” after a youth misinterprets the area being compared to Amsterdam where drug use is largely decriminalized. There are very little regulations in “Hamsterdam,” as the drug dealers may freely sell their products while law enforcement turns a blind eye, as long as there is no overt physical violence.

In short, dealers may deal, and users may buy and use without many legally enforced regulations or forms of social control. It should also be noted that Durkheim felt crime was a normal part of society. But, when the level of crime passed a certain threshold, then crime would no longer be considered normal and instead would be an indicator of society being truly sick. But before we get to the clip, let’s also account for Robert Merton’s rendition of anomie.

For Merton, anomie happened when there was a loss of means, meaning society didn’t care about the pathways by which people gained wealth, as long as they got wealthy (see also here). Or put another way, getting wealthy was more important than the processes by which someone made/got money. Likewise in this socially constructed environment of “Hamsterdam,” the means by which drug dealers make money is out of control. The goal is to profit, and there are no social morals that would otherwise guide people on how to reach those goals appropriately. Hence, the dealers (as directed by their superiors in the drug crews) will sell drugs to whoever will buy, something that’s facilitated in “Hamsterdam.”

In “Hamsterdam,” we see a combination of normlessness regarding both regulations and means…it’s total anomie for both Durkheim and Merton. Consequently, the levels of crime, and retreatism are astronomical. Even a seasoned character like “Bubbles” in this scene is deeply disturbed as he walks through the community. Of course as Major Colvin would like to point out, by decriminalizing drugs in one sector of the community, the rest of the community is much improved. Gang violence has subsided substantially across the broader sectors of West Baltimore. Unfortunately, unlike Amsterdam, public health-based social services are completely lacking in “Hamsterdam,” and only come in too late as Colvin’s social experiment is about to get shut down.

Okay, now let’s check out “Hamsterdam”:

By David Mayeda

In December 2012, The Lancet published an interesting article titled, “Healthy life expectancy for 187 countries, 1990—2010: a systematic analysis for the Global Burden Disease Study 2010” (to see full article, free registration is required). Using data from 2010, the authors’ analyses of studies illustrate a variety of health indicators across 187 countries. In particular the authors address the construct of “healthy life expectancy,” which speaks to the average number of years an individual within a certain country can expect to live from a certain life stage (e.g., from birth) in good health. By good health, the authors mean absence of disability, not acquiring a major disease, and I would presume a variety of other indicators (e.g., free of heavy violence and injuries).

The results, while perhaps predictable, are a telling illustration of global stratification. See visual, below (top image, labelled “A” represents male averages, and image below, labelled “B” represents female averages):

Pretty clear, countries across much of western Europe, Canada, Singapore, and New Zealand have the highest healthy life expectancies — their citizenries expecting to live relatively healthy lives up until their late 60s for males and early 70s for females. And then in Japan, males and females both can expect to live healthy into their early 70s. Of course there would be stratified patterns of inequality within those countries, but on average, their citizens’ healthy life expectancies are very high from a comparative global standpoint. In contrast, across much of Africa, in Afghanistan, and Papua New Guinea, males and females can expect to stay healthy only up to about their 40s or early 50s.

The authors also highlight Haiti, comparing it with Japan as the two countries with the greatest disparities: “Across countries, male healthy life expectancy at birth in 2010 ranged from 27·8 years (17·2—36·5) in Haiti to 70·6 (68·6—72·2) in Japan. Female healthy life expectancy at birth in 2010 ranged from 37·1 years (26·8—43·8) in Haiti to 75·5 (73·3—77·3) in Japan,” also noting the significance that the catastrophic earthquake had on Haiti in 2010. Japan of course also experiences natural disasters, such as earthquakes and tsunamis. However countries like Haiti are much less equipped to cope with earthquakes due to a lack of infrastructure and technology, ultimately tied to poverty, which many critical sociologists would say are tied further to colonial and neo-colonial relationships.

And then there are life expectancy rates as a whole. This a pretty busy table, including life expectancies and healthy life expectancies, for males and females, years 1990 and 2010 across all 187 countries. But the information is extremely useful in demonstrating how social inequalities across the globe result in peoples’ differing lived experiences along clear patterns.

So while we’ve seen both life expectancies and healthy life expectancies rise for males and females in most (if not all) countries from 1990 to 2010, the global disparities are still massive.

The disparities also speak to the concept of “slow violence” that I first saw here, and is further explained by Jacklyn Cock here:

“much destruction of human potential takes the form of a ‘slow violence’ that extends over time. It is insidious, undramatic and relatively invisible. By slow violence I mean what Rob Nixon calls ‘the long dyings,’ a violence that occurs gradually and out of sight, a violence of delayed destruction that is dispersed across time and space, an attritional violence that is typically not viewed as violence at all. Both environmental pollution and malnutrition are forms of this slow violence. Both instances are relatively invisible and involve serious damage which develops slowly over time.”

So we don’t think of these colossal disparities as examples of global violence. Instead we see them as unfortunate manifestations of poverty, perhaps reflecting a lack of leadership within the countries on the lower end of our globally stratified world. But really, mass social disparities are a form of violence in and of themselves because the less resources one has, the less they will be able to cope with things when crises emerge, whether the crisis be losing a job, having one’s house broken into, being in a car accident, or coping with a tsunami.

Furthermore, we know that when one lives in a community with higher levels of deprivation, certain crises are more common — physical health concerns, crime, educational concerns, un/under-employment. So the contributions to slow violence add up and have cumulative effects on individuals within those communities.

What I found additionally helpful about Jacklyn Cock’s article was how she spoke of sociologists’ social responsibility to the lived experiences of those coping with slow violence and heavier levels of overt violence/deprivation:

“Sociologists must be willing to extend their experiences into the lives of those they research. They must be willing to spend time in homes, mines, and factories, for extended periods of time. It is from this vantage point, from below, that social processes can be exposed and rigorously analyzed. Similarly, “organic public sociology’ ‘makes visible the invisible’ and works in close connection with a ‘visible, thick, active and often counter public.’ This involves emphasizing collective work and rejecting the call of C. Wright Mills ‘to stand for the primacy of the individual scholar.’ Instead, in this highly individualized neoliberal moment, sociologists have to stand in solidarity with the poor and the oppressed.”

Blogging and publishing in scholarly journals are hopefully helpful, but they sure aren’t adequate. Gotta get outa that ivory tower, cause confining oneself to academic circles is merely another pathway to reproducing inequality.

By David Mayeda

How can one provide sociological analyses of The Wire without bringing in the complex character, Omar Little? Little (well, Omar) is a Robin Hood-esque individual who incessantly steals drugs and money from Avon Barksdale’s crew. In retaliation, Avon has Omar’s partner brutally tortured and killed, leading Omar to hold an even greater obsession in ripping off the Barksdale crew.

In these two snippets from season 1, we first see Omar and his crew at night preparing to steal drugs/money (or “the stash”) from one of the Barksdale sites. Then the next day we see Omar and his crew try to carry out their plan. This is an excellent set of scenes one may use to better understand rational choice theory, which purports that individuals are generally rational, potential criminals, who would engage in crime if they could get away with it. In other words, we have a sense of free will and weigh the pros and cons that go into committing different crimes.

Rational choice theory, however, has a robust range of components. Again, all of us are potential criminals who…

  1. consider how crime is purposeful
  2. sometimes have clouded judgement about crime due to our bounded rationality
  3. make varied decisions based on the type of crime being considered
  4. have involvement decisions (initiation, habituation, and desistance) and event decisions (decisions made in the moment of a crime that should reduce the chances of being caught)
  5. have separate stages of involvement (background factors, current life circumstance, and situational variables)
  6. may plan a sequence of event decisions (a crime script)
Here are the scenes:

Note in particular Omar’s bounded rationality – how his judgement is clouded by his despise for the “Barksdale Crew”, as Omar’s crew asks at night in the car why they need to keep hitting up the Barksdale stash houses, even though more vulnerable targets exist. Also take note of the crime script that is supposed to work out well, but doesn’t, since Omar and company are not aware of the amount of firepower present in the stash house being targeted.

By David Mayeda

Edwin Sutherland is probably America’s most well known criminologist. His theory of differential association has been incredibly influential in criminology. It posits that crime – like any other type of behaviour – is learned. And there are some specific components to Sutherland’s theory of differential association, seen below:

  • criminal behaviours are learned
  • learning of criminal behaviours takes place through criminal teachers
  • learning of criminal behaviours is more effective when the teachers have close, intimate ties with the learners
  • crime techniques become more intricate and refined over time
  • criminal behaviours are defined and valued in a favourable light
  • motives for crime are different from motives behind non-criminal behaviours

It should be noted that Sutherland actually focused his theoretical positions on white collar crime – arguing that all types of crime, irrespective of their class parameters, were learned. Still, the theory can be applied in a variety of class contexts.

Now let’s look at another clip from The Wire that freaked me out … until we saw its ending. And as the ending of this short scene is revealed, pay attention to how the different components of Sutherland’s theory can be applied.

Real quick, a little background on what’s happening here. In this snippet, young Mike is being chased by mentors Chris and “Snoops” – two hardened, ruthless gang members. But eventually we learn that the chase is an exercise for Mike, in which he demonstrates the knowledge required to effectively engage in a gun fight:

Clearly at 2:45 of the video, we see that criminal behaviours are being taught and learned. There are older teachers/mentors (Chris and Snoops), and they have very close ties to Mike. In fact, Mike turned to Chris during a time of need to take care of a family problem Mike couldn’t cope with himself. In this scene, we also see Mike demonstrate that he is learning the more detailed dimensions of shooting targets (where to aim, from what distance). And Snoops’s smile at the end as she says, “Aiight, boy’s learnin”, illustrates the criminogenic behaviours – and Mike’s progress in mastering them – are being assigned with positive values.

Stay tuned, more sociology and The Wire coming up…

By David Mayeda

Back in the 1950s as criminologists began to more seriously explore the sociological causes behind crime, Robert K. Merton put forth his perspective through strain theory. Merton argued that mainstream society holds certain culturally defined goals that are dominant across society. In a capitalist society, the dominant goal that most people aim for is accumulating wealth. Merton further argued that this goal of becoming financially wealthy was so powerful that the goal of getting rich itself had become more important than the means by which one attained wealth. In other words, whether you got rich via conventional/legal means, or via unconventional/illegal means, it didn’t matter, as long as you got your coin. For Merton then, there was anomie (normlessness) regarding the means.

Merton furthered this perspective by providing a framework by which sociologists could typologise criminals and non-criminals – strain theory. Strain theory argues that one must consider if an individual rejects or accepts (1) society’s cultural goals (wanting to make money), as well as (2) the institutional means by which to attain those goals.

To this end, five typologies were established:

  1. Conformists, who accept the culturally defined goal of financial success, as well as the institutional means society defines as appropriate to reach that goal (e.g., advancing one’s education, steadily working, saving money). Conformists follow rules and believe doing so will pay off financially.
  2. Innovators, who also accept the culturally defined goal of financial success, but do not follow society’s rules (i.e., laws) in their pursuit of attaining wealth. Innovators may not have the means to attain financial wealth (e.g., not enough money to further advance education), and/or simply not believe in the law. Hence, innovators turn to crime.
  3. Ritualists are those individuals who do not believe they can attain the culturally defined goal of accumulating financial wealth, but who continue to do so through society’s acceptable cultural pathways simply because they are supposed to (e.g., going to work and school, despite feeling such actions will never pay off).
  4. Retreatists are people who reject the goal of financial wealth, as well as the means society deems acceptable to get rich. Hence people in this group escape, or retreat from society, often times through substance use.
  5. Rebels are the last group who redefine society’s goals and create new institutional means of pursuing their unique goals. Rebels work outside of the established system. (See the framework mapped out by clicking here):

Okay, so let’s apply this theory to some examples from HBO’s television drama series, The Wire. In this first example, we see two snippets from Season 3 when characters Avon Barksdale (a west Baltimore drug kingpin recently released from prison) and his right-hand man, Stringer Bell, debate how they can reclaim their top “real estate” (or “corners”), where they would have the younger members of their crew sell heroine. Though not seen in these snippets, a new player named Marlo has entered the west Baltimore market and violently taken the most lucrative corners from Avon’s crew.

Listen to Avon and Stringer Bell discuss the pros and cons of going against Marlo versus trying to work with him. And more importantly listen to Avon – despite already having achieved extensive wealth – state how he would rather habituate by remaining a gangster, or from Merton’s perspective, an innovator. In contrast, listen to Stringer Bell push to work with Marlo and eventually desist from the drug trafficking scene, making “straight money,” much more so as a conformist.

Let’s also examine two other characters from The Wire – “Bubbles” and Johnny. In the early parts of this series, Bubbles and Johnny would be defined predominantly as retreatists, who aspire incessantly to get high on heroine. But over the series, Bubbles changes. As the two comrades walk down the street in this scene, listen to Bubbles talk of wanting to desist by becoming a “snitch” for the police. In other words, he is working towards becoming a conformist. Johnny, however, wants none of this:

Johnny temporarily convinces Bubbles to help him rip off the man on the ladder. And note in this particular scene,  Bubbles and Johnny are both innovators – working to get money via illegal means. Still, I would argue Johnny’s status stands predominantly as a retreatist, who innovates through petty crime simply to feed his retreatist addiction (i.e., retreat from society). And again, while Bubbles is an innovator in tandem with his friend in this scene, he is clearly working towards a life of conformity, seen more clearly when he disappears and decides not to take the money.

More analyses through The Wire on the way..