"To tell the truth, to speak friendly, warm, and gently and to talk only when necessary (from The Bigview )." When conducting data analysis, the "numbers" often spring forward as the primary focus of attention. The language we use when formulating and conducting analysis is also a critical foundation. We can build on one of the concepts we discussed in Right Vision, "commitment to framing questions and presenting results in a non-stigmatizing way, especially when focused on those with less power". One of the specific ways that we can put this intention into practice is through our use of language. Science is popularly characterized as being objective, but we have to develop an active awareness of how our place in the world influences how we see and describe things.
For example, I grew up with lactose intolerance being defined as a racially associated illness or deficiency. This may seem accurate if you are defining normality as the dominant traits of populations of European heritage, but from a larger human perspective, dairy products as a regular part of the diet of adults is the exception, not the rule. Lactose intolerance is the normative state of the species and is not intrinsically associated with ill health.
In public health, we commonly invoke biased social constructs such as "births to unmarried mothers" and "teen mothers". Unless our species has developed parthenogenesis, births involve two parents. By only describing mothers, and making recommendations and policy analysis directed towards women, we add life to a long standing misogynist framework that puts women as the gatekeepers of men's sexuality.
In our language, we should strive to be slow to pathologize differences, recognize and acknowledge the limits of our perspective, use descriptive language and concepts that we would be comfortable sharing with the subjects of our analyses as well as our colleagues or sponsors.
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