Credit: west.m

Last week thousands of British students descended on London’s Conservative Party headquarters to protest drastic increases in tuition fees. Despite protestations from Liberal-Democrat leader Nick Clegg, the changing fee structure will make education much more costly—potentially three times more costly—for all students. Proponents of the fee hikes trotted out the usual lines about parental support, reasonable payment schedules and personal responsibility, but the images of tens of thousands of students smashing windows, occupying buildings and throwing documents and chairs from the roof of an office tower demonstrated to the world that many students—the ones who will actually have to deal with the changing funding regime—disagree emphatically.

A recently released Quebec study lends support to the protesters’ position and, for the umpteenth time, demonstrates how tired and fallacious the rationales for fee hikes are. The kids, it turns out, are not all right.

• Including grants, co-ops, financial help from parents, scholarships and part-time employment roughly half of all Quebec undergraduates live on less than $12,200 a year
• 40 percent receive no money from their parents, and 65 percent do not live with their parents
• 80 percent of students have a job, half work more than 15 hours per week in addition to their full-time studies
• 22 percent have credit card debt (average debt load: $2,700)
• More than half of all students receive no financial aid
• Just under 60 percent have housing costs that eat up more than 30 percent of their income.

The situation in Quebec, to be sure, is different from the one in England—but not so different. Students in each place are being asked to take on more debt, work harder and longer hours, spend more on housing and less on things like food, books, and entertainment. And all in an economy that cannot promise a decent wage, job security or benefits at the end of it all. I, for one, doubt that the most recent occupation will be the last.

Consider this: the population growth of racialized or non-white groups continues to outpace that of white Canadians. This has created a shift in the demographic balance of the Canadian mosaic, with our population on its way to becoming a “minority majority.”

According to Statistics Canada, by 2031, over 70 percent of Canadians living in Toronto, Montreal, and Vancouver will be from a visible minority or racialized group. Already, almost half the population in the Greater Toronto Area is a visible minority. Yet we are not seeing an equivalent shift in the halls of power: in business and in government, visible minorities—particularly African-Canadians—still represent a small fraction of the decision-makers relative to their overall population.

In April, a report by the Law Society of Upper Canada looked at the legal profession relative to others and made the following observations: “In Ontario in 2006, members of a visible minority accounted for 30.7 percent of all physicians, 31.7 percent of engineers, 17.6 percent of academics and 11.8 percent of high-level managers, compared to 11.5 percent of lawyers.”

A recently released study, entitled “DiverseCity Counts: A Snapshot of Diverse Leadership in the Greater Toronto Area,” showed that while some sectors are doing better at reflecting the general makeup of the population, visible minorities are underrepresented in leadership positions. Today, visible minorities comprise 49.5 percent of the population, but only 14 percent of senior-level leaders.

The implications of this imbalance will only become more significant as the population continues to shift. Canada must demonstrate the potential of harnessing the best of all of our peoples. Diversity in the leadership of our institutions matters. Far from being a form of tokenism, a significant increase in the number and diversity of visible minorities at all levels of leadership is essential to Canada’s competitiveness.

In May, Governor General Michaëlle Jean addressed business, academic, and socialsector leaders in a speech to the Canadian Club. She told the audience that “saying yes to diversity is saying yes to modernity, to opportunity, and to the very future of our country.” There is an economic case for embracing diversity: to create a “brain gain” by recruiting, hiring, mentoring, developing, and retaining a qualified and diverse workforce. Imagine the dividends for Canada’s global competitiveness when all its citizens have an equal opportunity to lead, to innovate, and to contribute to our social, economic, cultural, and political landscape.

The DiverseCity study also found that visible minorities are underrepresented in the media, accounting for only 19 percent of appearances by broadcasters, reporters, print columnists, subject experts, and commentators. Diversity in media leadership and representation of visible minorities is improving incrementally, but larger gains are needed. Why do we not see or hear from more visible minorities in daily coverage? How long must we wait for media outlets to do the research and start assigning these stories?

One initiative to improve the coverage of racialized minorities is DiverseCity Voices, a new electronic database of experts who are also visible minorities. Journalists can turn to the website to find underrepresented leaders who are able to provide commentary and opinion on current affairs.

Since joining the website, I have appeared in a variety of local and national print and radio, television, broadcast, and social media outlets, providing my opinions on subjects ranging from the Olympic Games, the G20 summit, and Africentric Schools. I’ve received more calls from journalists looking for comment, and that’s important to me. Young people become what they see, and role models of all backgrounds need to be seen and heard.

Sociologist John Porter’s Vertical Mosaic, published in 1965, remains the touchstone for a deeper understanding of the structure of Canadian society. Porter’s findings portrayed Canada as a hierarchical racial pecking order, with attendant consequences for social mobility, access to power, and economic success. In the Canadian mosaic, whites were (and still are) the dominant culture at the top of the heap. Fifty years on, Porter’s study still rings depressingly true. But there is reason to be optimistic.

With a conscious effort to create and sustain diversity in all our institutions, it is possible that Canada’s vertical mosaic will be replaced by one that is inclusive, linear, and beneficial to us all—no matter where we come from or the colour of our skin.

But if I’ve been reluctant to argue this at length, it’s because StatsCan has never done much to earn my goodwill. As a journalist interested in statistics, I have come to expect frustration and disappointment when dealing with StatsCan. That’s why I hope that we can take this opportunity to talk about how it could be better—rather than fighting blindly for the status quo.

The most serious problem with Canada’s data authority is access to data, or more accurately, the lack thereof. And all of the restrictions on access, going back many years, have been justified by some extremely strange concerns around privacy. Sound familiar?

On Statistics Canada’s website, you’ll find a variety of publicly-available summary statistics. If you’re willing to pay a more or less reasonable fee, you can buy access to other simple time series—the unemployment rate going back several decades, for example. But while subject- and neighbourhood-level summaries (or “metadata”) can be useful in some contexts, the most valuable census product is “microdata”—individual-level results, coded and cleaned up so that anyone with statistical software can create their own metadata or run regressions.

A microdata record for a given household can contain a lot of personal information. But I’ve been up to my elbows in U.S. Census microdata, analyzing some of the most sensitive information it contains—right down to sexual orientation and income—and I’m confident asserting that nobody has ever recognized themselves or a neighbour in a census record.

That’s because when it’s creating public-use microdata, the U.S. Census Bureau modifies records in specific ways that further obscure people’s identities without affecting researchers’ analysis. They only provide broad location information, for example. As a result, public-use records are specific enough to be useful, but not refined enough to be identifiable.

That, presumably, is why the U.S. government is comfortable posting large samples from its public-use microdata on the ungated web. Anyone can download detailed census microdata for 14 million Americans, and even more exhaustive American Community Survey and General Social Survey data.

Public use microdata samples exist in Canada as well, but you have to be affiliated with an approved university to access them, and the data is substantially degraded in the name of privacy. There is a lot that you can’t do with the Canadian data, because so many useful variables aren’t included, and because 2.7% of the Canadian population is a pretty small sample if you’re already researching a tiny minority, like, say, lesbian families. That’s how, as an undergraduate, I ended up studying the American census even though I could download Canadian microdata.

Lucky for academics, if not anyone else, StatsCan does provide more detailed microdata at a small number of physical locations, the Research Data Centres. Applying for access to the centres is a Byzantine process, only open to students and academics. (Today, as a journalist, I wouldn’t even be considered.)
Researchers must prepare a lengthy proposal for StatsCan, laying out their objectives, and describing and justifying their methodology. Proposals must include information about the accomplishments of the applicants, including “identifiable contributions made by the applicants to the advancement, development and transmission of knowledge related to the disciplines supported by” the Social Sciences and Humanities Research Council.

If approved, researchers have to go through a security screening process and sign a contract with StatsCan. The process can take years, which means that only academics prepared to devote their life to social research ever have access to the RDCs. But a form that asks how many bedrooms you have is fascist, right?

Even government employees have to jump through hoops to access StatsCan’s most useful data: all requests are assessed by a provincial or territorial representative on the Federal-Provincial-Territorial Consultative Council on Statistical Policy:

The request for access is submitted to the Program Manager of Statistics Canada’s Research Data Centre Program who coordinates a review of the proposal by Statistics Canada subject matter experts. The review is completed within 10 working days. If it is determined that Statistics Canada can quickly and efficiently carry out the work, the Departmental representative will be informed of this and of the associated cost to complete the work. However, if Statistics Canada does not have the resources to complete the work quickly and efficiently, the provincial/territorial employee identified is eligible to become a “deemed” Statistics Canada employee, under Section 10 of the Statistics Act, for purposes of completing the work.

Now that’s small government at work.

The upshot of all this is that journalists, bloggers, businesspeople, students, and anyone else with a copy of SPSS and a dream ends up studying the United States rather than Canada. Consciously or not, that influences Canadian identity. It drastically reduces the rewards that we could reap in return for all of the money and time spent administering the census.

It’s also colossally unfair. Sure, the long form means giving up some privacy, and yeah, it’s a hassle. But I don’t need much in return—I just want the right to access the results myself, without getting a PhD and then staring down StatsCan’s bureaucracy. I also want smart people everywhere—not just a few academics—to be able to refine that data gold mine into information that can improve my life. If the underfunded, under-siege U.S. federal government can do it, then surely Ottawa can try.

If Maclean’s was looking for someone to explain Canada’s teenage girls to their parents, Sax was a strange choice. He does have a PhD in psychology from 1980, but he primarily interprets and popularizes research rather than doing peer-reviewed work himself. Unfortunately, his interpretations are pretty controversial.

A blog called Language Log has criticized Sax for over-interpreting and distorting research on gender differences. This stands out to me because I know Language Log to be home to particularly smart take-downs of bad statistics. The New York Times also published a fairly critical profile of Sax a couple years ago.

So I shouldn’t be surprised to find Sax up to his usual tricks in this Q&A. Take his claims about self injury:

…if you look at the literature, you see that more than one in five girls is cutting herself and/or burning herself with matches. […] In a very well-executed study published in the Canadian Medical Association Journal two years ago, a demographically representative sample of young people 14 to 21 years of age was surveyed in Victoria, and there was an overall prevalence of roughly 16 percent. Although in the abstract there’s no mention of sex differences, if you pull up the tables you see that only eight percent of boys but 24 percent of girls were cutting or burning themselves.

In fact, that’s not exactly what the paper found. (You can read it here.) Sixteen percent of young people and 24 percent of girls had, at some time, injured themselves. But the way this statistic is repeated presents two problems. First, Sax implies that all self injury in these papers is cutting or burning, when in fact the authors also measured some kinds of drug and alcohol use, and other behaviours. This is an understandable memory lapse. But second, and more importantly, Sax’s statistics are for youth who had ever hurt themselves. When we look at how many youth had injured themselves more than three times, prevalence falls to six percent. (The paper doesn’t provide a gender breakdown for that smaller group.)

The difficulty here is Sax’s verb tense. The fact that he says “one in five girls is” and then later “24 percent of girls were” suggests an ongoing, long-term problem. As unpleasant as it is to imagine, I think we can accept that a large number of teenagers try out self harm, and that this is quite different from someone who injures repeatedly, over a long period of time. It is the latter scenario that Sax goes on to describe in titillating detail:

The girls themselves tell you, “I cut myself because it’s real, it’s not fake.” It’s not a cry for help: most girls don’t want adults knowing they’re cutting, which is why they cut in places we won’t see, like high up on the inner thigh. And they don’t want to kill themselves. There’s research which is quite astonishing to many people: when girls cut themselves, they are getting a release of endogenous opiates—they’re actually getting high.

This is a small misinterpretation, but it is important. A surprising result has unusual power in this sort of piece—it stops readers short, overturns their assumptions, and encourages them to reassess the rest of the article’s claims. And it’s especially disappointing coming from a magazine that just last year published a comprehensive package on how well teenagers are doing:

In light of these facts, [Reginald] Bibby [sociology at the University of Lethbridge] expects strong resistance to his findings from the very teen crisis apparatus he partially credits with all the good news. “The experts act almost annoyed when you suggest kids are actually looking a little better,” he says. Some of that blowback stems from genuine difference of opinion. But a lot grows out of popular wisdom coming out of the United States.

Unfortunately, with this piece Maclean’s has uncritically repeated that misleading popular wisdom. And from a cover this sensationalist, I think we have a right to expect more.

If only journalists displayed this much facility with stats. XKCD comic by Randall Munroe.

A year of layoffs and anaemic ad buys has given journalists an excuse to turn inwards like never before. By now, even folks outside the industry must be sick of hearing about the Future of Journalism — my own fervent hope is to never read another article about social media for reporters. But I do think that an instinct for self-improvement is useful, so I’m going to add something else to the agenda — call it a Margin of Error manifesto. I’d like to talk about statistical literacy.

I know that it’s a bit predictable, even self-serving, to argue that everyone should have a skill that you have already developed. And I hate to add another job description to a list that is rapidly becoming unmanageable. Increasingly, it seems that we are all expected to be programmers and photographers, designers and copy editors, fact checkers and fundraisers. I’m not sure what to make of this job ooze, as an economist — we know something about the value of specialization — but so long as we’re all marginally employed and learning new skills, let’s try to pick up this indispensable one.

Craig Silverman has been saying smart things about journalism since before it was fashionable, and he is forceful about the importance of numeracy. But my point is not just about knowing how to add. When I say that we need to be literate, I mean that journalists should be comfortable enough with statistical methods to skim an academic paper, poll, or piece of market research and know whether the numbers say what our sources claim. We should have a clear hold on what it means to control for something, and when and why something is statistically significant. We should be able to compare contradictory studies. A couple rigorous university-level courses in social science methods would do the trick, more or less, but reading a few textbooks might be even better.

Unfortunately, most journalists don’t have a single course in methods, or any math past high school. We are, for better or worse, an industry of math-phobic English majors. And yet we report on statistics almost daily. Even crime reporters have to throw in the occasional paragraph on whether the murder rate is going up or down, and lifestyle columnists just love to write about neuroscience studies.

A few things happen when you have to report on something that you barely understand. You are forced to trust the researchers absolutely — whenever private companies release their own research, that’s a risk. You introduce errors by paraphrasing. You fail to communicate which results are suspect and which are nearly indisputable. Everything is reported as a breakthrough, because no researcher will admit to an incremental result. You can’t put contradictory studies in context, which drives readers nuts — who can remember whether red wine is good or bad for you, anyway? What are we supposed to eat, if everything causes cancer?

My sense is that political reporters develop some expertise on polling, and health reporters are getting serious about understanding drug and diet trials. A select group of journalists are making data what they do, and the New York Times has been doing some beautiful work with online infographics. But if you type “study” into Google News and scroll down, there is plenty of depressing nonsense to be found.

In August, the New York Times declared this the golden age of statistics, quoting Google’s resident math geek Hal Varian. Thanks to the internet, more data is more available than ever before. Statistics is only going to become more important. We can only hope that we will be able to cover it. Without statistical literacy, we will just be writing fiction.

The charter school movement is enjoying something of a renaissance in the United States. Charter schools—which receive public funding but are privately run, thus removing some features of normal public schools, notably established teachers’ unions—are one of President Obama’s priorities.  They are also the cause du jour for New York City’s hedge fund managers. Education policy wonks are understandably interested in whether charter schools really help students failed by the public system.

I am sceptical about the benefits of private sector involvement in public schools, but I’ve still been watching the debates with some interest. There has not been much research on Canada’s only charters, in Alberta, so most of what I read is from the States. That’s why I came across this post on Eduwonk.

In it, Andrew Rotherham observes that charters are over-represented in the U.S. News top 100 high school rankings. Since Rotherham introduced the post with “here’s one way to think about charter performance” I thought he meant this as evidence that charters are better schools in general. He has since responded to my comment, and clarified that he meant to make a narrower point. But I’d still like to explain my original objection, because there is a bigger lesson here.

Let’s imagine that we looked at a ranking of the best 100 schools in the US, and found that all 100 were charter schools. We might conclude that charter schools, on average, are better schools. But it could also be that charter schools are more variable—more likely to be very good, but also more likely to be very bad. Maybe the bottom 100 schools are all charters as well. Since we don’t have the full data set, we can’t tell.

What we’re talking about is the difference between mean and variance. Mean is a fancy word for what you know as the average—an estimate of the centre of some data. Variance is a measure of how spread out that data is. It helps to look at the graphs below.

These curves represent an imaginary data set. The height of the curves shows how many schools are at each level of quality, which moves from low to high along the bottom. On these symmetrical curves, the mean is the same as the mode, or the quality level where the curve maxes out. The variance expresses how wide the curves are.

Now let’s imagine that we’ve taken a sample of the highest quality schools – say, everything to the right of the vertical dotted line. We’ve found that every school in our sample is a charter. In this graph, that’s because the distribution for charter schools is further to the right – they are, on average, better schools:

But in this second graph, we find a similar sample—all charter schools—even though the mean for both types of schools is the same. All our data reflects is that charter schools are more variable. A lot of them are very good, but a good number are very bad as well:

The way an introductory statistics textbook would put this is that you can’t select your sample based on the dependent variable. If you want to know what sort of diet will help you live longer, you can’t just interview your oldest relatives. If you want to know what sort of training will make you run faster, you can’t just talk to Olympic gold medalists. You won’t have the full picture, and everything that is special about these high achievers could be true of other, less successful eaters or runners. Informally, and in the press, we make this mistake all the time.

I said that this data set was imaginary for a reason – you shouldn’t draw any conclusions from my hypotheticals. A lot of different factors make measuring charter school performance difficult, so research is mixed. But there are reasons to believe that the U.S. News data might at least in part be reflecting higher variance. If charter schools have to follow fewer rules, it makes sense that they would vary more than normal public schools.

This isn’t something that charter advocates dispute. As two Eduwonk guest bloggers put it in October: “Say you set out to improve your mother’s beloved spaghetti sauce recipe. […] You try ten different variations.  Despite your best efforts, three are worse than the original.  Five are no better, but two are markedly superior.  On average, the new batches are a little worse than your mom’s. But—would you say your experiment was a failure, or a success? It really depends on what you do next.” Proponents of charter schools want to shut down bad schools, and learn from good schools. As Rotherham wrote in his response, “in practice there are elements of state policy that can move the quality curve substantially to the right.”

This might be why education reformers are so focused on a relatively small number of successful models, like the Knowledge is Power Program and Green Dot Public Schools. These reformers are fine with high variance, and interested in the upper tail of the distribution. But over the long run, they will also need to prove that they can move the whole curve to the right.

• Year by which G8 countries pledged “universal access” for HIV/AIDS treatment, prevention, and care: 2010
• Estimated number of people, globally, currently receiving that care: 4,000,000
• Estimated number of people, globally, still waiting on that pledge: 5,000,000 *
• Percentage of Canada’s population that is Aboriginal: 4%
• Percentage of new Canadian HIV/AIDS patients who are Aboriginal: 10% *
  
• Estimated number of Canadians living with HIV/AIDS as of the end of 2008: 65,000
• Percentage increase in number of Canadians living with HIV/AIDS between 2005 and 2008: 14%
• Factor by which an Aboriginal Canadian was more at risk to contract HIV/AIDS in 2008, compared to the general population: 3.6x
• Estimated percentage of Canadian HIV-positive gay men who remain unaware of their infection: 19%
• Estimated percentage of Canadian HIV-positive heterosexuals who remain unaware of their infection (see comment below): 35% *
  
• Percentage of Catholics surveyed in Ireland, the U.S., and Mexico, respectively, who agreed that “the church’s position on condoms is wrong and should be changed”: 79%, 63%, 60% *
  
• Estimated amount spent on marketing costs to promote the (Product) RED campaign in its first year: US$100 million
• Amount that Ad Age reported was raised by the campaign for that year: US$18 million *
• Total amount (Product) RED reports it has raised to date, according to a July 2009 blog post: $130 million *
• Year in which HIV/AIDS infections peaked worldwide: 1996
• Global percentage decline in new HIV/AIDS infections in the last eight years: 17% *
• Estimated funds required to respond to the global HIV/AIDS epidemic in 2010: US$25.1 billion *
  
• Amount by which 2010 funding is currently estimated to fall short of that amount: US$11.3 billion *
  

What's the real value of a university education? The answer's more complicated than you might think. Illustration by Graham F. Scott.

Welcome to Margin of Error. Each month, I will be picking apart some number or statistical problem in the news or on my mind. I will draw on my own modest knowledge of econometrics — the statistics of economics, lately being applied to all kinds of problems — and interview the occasional expert. If you spot a questionable number that you’d like me to pick apart, send a tip to marginoferror (at) this (dot) org.

Thanks to midterms, university students across the country are too busy to read this. That’s probably just as well, because I’m here to share a story that might upset them — the degrees they are cramming for probably aren’t worth as much as they think.

It’s no secret that people with university degrees, on average, make more money. If you’re interested in how much more, you might look up some census data, take the average wages for someone with a degree, and subtract the average wages of someone without a degree.

But if you are following along on the back of an envelope — aren’t you proactive! — I hope that you won’t assume that you’ve figured out the effect of a university degree on income. It doesn’t matter how many times we say that correlation does not imply causation — reporters everywhere keep falling down the rabbit hole. And it’s hard to blame them, with oversimplified press releases like this coming out of Statistics Canada.

To understand what is wrong with this simple calculation, it helps to imagine an ideal experiment that could tell us how much going to university raises one’s income. Let’s say we could take a large group of high school graduates, and randomly assign half of them to complete university. Travelling several decades into the future, we would ask them about their income, and compare the two groups.

The difference between doing this and using census data is the random assignment. In real life, the choice to attend university is far from random. It might have to do with your ability to pay tuition, parents’ education, work ethic, or intelligence. If students who are likely to make more money anyway are also more likely to go to university, then we cannot tell what we are measuring – the initial advantage that secures a place in university, or the impact of the university education itself.

Luckily, there are some alternatives to this impossible experiment. Using statistics, we can control for just about anything that we can observe. Tons of research has been done on education and income. Close to home, a paper published in August in the Canadian Journal of Economics, by Vincenzo Caponi and Miana Plesca, focused on data from Statistic Canada’s 1994 General Social Survey. (The official version is gated, but a draft is available for free.) This paper looks at 3,274 people, all high school grads aged 17-65, not students, and employed in 1994.

If we simply look at averages, in this survey, men with university degrees made 59 per cent more per hour than men with only high school diplomas. For women, the gap was 50 per cent. But for all the reasons we’ve discussed, the authors do not do this — they use control variables. With basic controls — immigration status, province, marital status, education, work experience, etc. — the university wage premium drops to about 42 per cent for both genders.

Then the authors use a fancy procedure called propensity score matching. This lets them compare workers with different levels of education that nonetheless have a similar background, taking into account, among other things, parents’ education and number of siblings. With matching, the university wage premium drops further, to about 35 per cent for men and 39 per cent for women.

Unfortunately, there is a bigger problem. What if university students posses some inherent intelligence and motivation — let’s call it ability — that is difficult to measure? Caponi and Plesca argue that ability is inherited, so they control for it by using parents’ education. Most studies from the US control for ability by using test scores. But it is hard to believe we could ever control for, for example, someone’s drive to land a high-paying job. If the parts of ability that are impossible to measure also help you get into university, than we’re still overestimating the impact of university.

That isn’t to say that a degree is useless. First of all, the premium is still large, even with controls, so we can guess that in a perfect experiment it would not disappear. University might also benefit students in ways that do not affect income. Increasing the number of university graduates in Canada might be good for our culture or economic growth.

But there’s something to be learned here, whether you are studying for midterms or not. All sorts of things can cause two numbers to be correlated. When you hear an argument about causality, you should always think about what has been left out.

The New Democratic Party convenes today in Halifax for its federal convention, and one of the hottest questions is whether the party will drop the “New” from its name. Sure, there’s a bunch of boring old policy meetings and stuff, to, you know, lay out a vision for the country and junk, but there’s something irresistible about the razzle-dazzle of a rebranding. Nick Taylor-Vaisey jumped on this story weeks ago, way before the mainstream media caught on, but now everyone’s got an opinion.

Anyway, the reason I’m filing this under our cheery Friday FTW section is that it actually has shoved Canadian progressive politics into the spotlight again, which is crucial. (I’ve reposted James Laxer’s important and controversial This cover story from last year on the future of the NDP, just to stoke the fires a little more.) The prospect of a name change has fostered some important talk in progressive circles about what exactly we want from a left political party: do we want uncompromising, principled ideological stances, regardless of the electoral outcome? Is it actually important to pursue power? Are these goals mutually incompatible? Have we been seduced by the success of the Obama electoral machine and just want to grab some of that excitement for ourselves in Canada? I don’t know, but I’m sure glad we’re talking about it.

The nice thing here is that there have been all kinds of useful, necessary questions asked, and the stakes are actually pretty low at this point. Judging by a hilarious poll commissioned by the Canadian Press and released yesterday, it really doesn’t matter:

Of about 1,000 Canadians surveyed, about one-third each believe the name change is a good idea, a bad idea, or just don’t give a shit. Practically no votes ride on the decision; if there’s a name change, lefties aren’t going to flee the party, and righties aren’t going to flock to it. In other words, maybe we can get the whole name-change thing out of the way, whatever the decision is, and talk about the truly important issues.