Archive | January 16, 2015

The mismeasurement of humans: classification as “othering”

I was part of a short, but interesting discussion last night regarding this very good article on the political implications of data analysis. The argument made (assuming I understood it correctly) was simply that statistical measures are inherently ideological since they impose a particular view of the world from one social group (us, the elite) on another (the non-elite). She takes this further, stating that though the voice of the elite can be heard through anecdotes (and opinionated blog posts), the experience of the non-elite relies on statistics and numbers. Statistics, then, is the language of power.

The conversation went further to discuss the implications of statistical methods themselves, particularly the measures of central tendency: the mean, median and mode. With perfectly symmetrical data, these measures are all the same, but, of course, no set of data is perfectly symmetrical, so that the application of each will produce different results. Though any responsible statistician would make statements of assumptions, limitations and appropriateness, with politics, these statements are overlooked and the method chosen is often that which best supports one’s political position, asking for trouble.

Moreover, the measure of central tendency itself in inherently flawed since it concentrates on the center and silences the extremes, supporting the status quo, or so it was argued. The choice of measure, I would argue, depends on the goals of the particular study. For example, a study which sought to determine if average graduation rates lower for blacks than whites would necessarily use a measure of central tendency, while a study on which students in a particular school are the least likely to graduate might look at outliers and extremes.

Either way, I agreed with the writer that, no matter what, we are influenced by our ideology. However, there is a difference between performing a study which seeks to maintain impartiality for the greater good and one which seeks to deceive in order to merely win a political battle, particularly among those who benefit from marginalizing, for example, the poor and disenfranchised.

However, I found this passage quite interesting and it can be applied to a post on this blog regarding what we do and don’t know about the poor:

Perhaps statistics should be considered a technology of mistrust—statistics are used when personal experience is in doubt because the analyst has no intimate knowledge of it. Statistics are consistently used as a technology of the educated elite to discuss the lower classes and subaltern populations, those individuals that are considered unknowable and untrustworthy of delivering their own accounts of their daily life. A demand for statistical proof is blatant distrust of someone’s lived experience. The very demand for statistical proof is otherizing because it defines the subject as an outsider, not worthy of the benefit of the doubt.

Part of my academic work focuses on the refinement of measurements of poverty. I am keenly aware of the “othering” of this process and how these measurements use a language of the educated elite (me) to speak for the daily experiences of people not like me.

This “othering” is not limited to statistics at all. Even merely referring to “the poor” is a condescending labeling of a group of people who are mostly powerless to speak for themselves within global power structures. Moreover, “the poor” ignores the diverse and varied experiences of most of humanity.

When I first entered the School of Public Health at UM, I was extremely uncomfortable with the language used in studies of ethnicity and public health in the United States. Studies would simply throw people into simplistic categories of black, white, hispanic, asian and “other” (whatever that is), ignoring the great diversity of people within, for example, urban slums. The method of categorization seemed to be a horrible anachronism and bought back awful memories of Mississippi. Simply putting people into neat categories risked continuing an already divisive view of the world.

However, the more I thought about it, the method is justified since we are looking at the effects of a racist view of the world on the very people who are the most burdened by it. Certainly, there are better ways of viewing the world, but when criticizing social power structures, it can be advantageous to speak its language. I still don’t like it, but I’m at least more understanding of it.

It’s a fine thread to walk. On the one hand, as advocates for “the poor,” we have to work within the very structures which oppress, exploit and ignore them. To succeed, however uncomfortable it may be, we may be required to adopt the language of those structures. On the other, we must remain aware of the potentially dire implications of the ways in which we describe those we advocate for and how they can be misused.

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