Genders are categorizing heuristics, not categories unto themselves. As such, they fall prey to the same problem all clustering algorithms do: poorly segregated datasets yield many false positives and negatives, and there may exist more viable and clearly separated clusters when one increases the number of potential clusters. When the dataset is {All human behavior}, at best they can be used to determine centrality of certain clusters, but not their boundaries.

If I tell you what I am, I am telling you what centerpoint to use to estimate who I am, not what my limits are.

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2 Responses to Genders Are Categorizing Heuristics

  1. Rose says:

    This is seriously brilliant. One interesting thing about using humans as classifiers: the easiest way to get decent inter-rater reliability is having finely defined taxonomies.

  2. Elmo says:

    This gives me a very concise, rigorous and scientific way to describe how I view gender, and to confuse the hell out of most people (when even I’m not very familiar with the terms used) 😉

    Thank you very much!

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