X-Git-Url: http://unremediatedgender.space/source?p=Ultimately_Untrue_Thought.git;a=blobdiff_plain;f=content%2Fdrafts%2Fsurvey-data-on-cis-and-trans-women-among-haskell-programmers.md;fp=content%2Fdrafts%2Fsurvey-data-on-cis-and-trans-women-among-haskell-programmers.md;h=0000000000000000000000000000000000000000;hp=6506081ce5736e10ade82c4014ec8754f1010448;hb=74f590a1e836b2c28238526c8b75c7270298e39a;hpb=865c5f3ae97dfaae4dec207b52adcd394b1a1cd0

diff --git a/content/drafts/survey-data-on-cis-and-trans-women-among-haskell-programmers.md b/content/drafts/survey-data-on-cis-and-trans-women-among-haskell-programmers.md
deleted file mode 100644
index 6506081..0000000
--- a/content/drafts/survey-data-on-cis-and-trans-women-among-haskell-programmers.md
+++ /dev/null
@@ -1,67 +0,0 @@
-Title: Survey Data on Cis and Trans Women Among Haskell Programmers
-Date: 2021-01-01
-Category: other
-Tags: Haskell, sex differences, Python
-Status: draft
-
-Stereotypically, computer programming is both a predominantly male profession and the quintessential profession of non-exclusively-androphilic trans women. Stereotypically, these demographic trends are even more pronounced in "niche", academic, or hobbyist technology communities (_e.g._, Rust), rather than those with more established mainstream use (_e.g._, JavaScript).
-
-But stereotypes can be _wrong_! The heuristic process by which people's brains form stereotypes from experience are riddled with biases that prevent our mental model of what people are like from matching what people are _actually_ like. Unless you believe [a woman is more likely to be a feminist bank teller than a bank teller (which is _mathematically impossible_)](https://en.wikipedia.org/wiki/Conjunction_fallacy), you're best off seeking _hard numbers_ about what people are like rather than relying on mere stereotypes.
-
-Fortunately, sometimes hard numbers are available! Taylor Fausak has been administering an annual State of Haskell survey [since 2017](https://taylor.fausak.me/2017/11/15/2017-state-of-haskell-survey-results/), and the [2018](https://taylor.fausak.me/2018/11/18/2018-state-of-haskell-survey-results/), [2019](https://taylor.fausak.me/2019/11/16/haskell-survey-results/), and [2020](https://taylor.fausak.me/2020/11/22/haskell-survey-results/) surveys include optional "What is your gender?" and "Do you identify as transgender?" questions, as well as the anonymous response data. 
-
-I wrote a script to use these answers from the CSV response data for the 2018–2020 surveys to tally the number of cis and trans women among survey respondents. (In Python. Sorry.)
-
-```
-import csv
-
-survey_results_filenames = [
-    "2018-11-18-2018-state-of-haskell-survey-results.csv",
-    "2019-11-16-state-of-haskell-survey-results.csv",
-    "2020-11-22-haskell-survey-results.csv",
-]
-
-if __name__ == "__main__":
-    for results_filename in survey_results_filenames:
-        year, _ = results_filename.split("-", 1)
-        with open(results_filename) as results_file:
-            reader = csv.DictReader(results_file)
-            total = 0
-            cis_f = 0
-            trans_f = 0
-            for row in reader:
-                total += 1
-                # 2018 and 2019 CSV header has the full question, but
-                # 2020 uses sXqY format
-                gender_answer = (
-                    row.get("What is your gender?") or row.get("s7q2")
-                )
-                if gender_answer == "Female":
-                    transwer = (
-                        row.get("Do you identify as transgender?") or
-                        row.get("s7q3")
-                    )
-                    if transwer == "No":
-                        cis_f += 1
-                    elif transwer == "Yes":
-                        trans_f += 1
-            print(
-                "{}: total: {}, "
-                "cis-♀: {} ({:.2f}%), trans-♀: {} ({:.2f}%)".format(
-                    year, total,
-                    cis_f, 100*cis_f/total,
-                    trans_f, 100*trans_f/total,
-                )
-            )
-
-```
-
-It prints this tally:
-
-```
-2018: total: 1361, cis-♀: 26 (1.91%), trans-♀: 19 (1.40%)
-2019: total: 1211, cis-♀: 16 (1.32%), trans-♀: 16 (1.32%)
-2020: total: 1348, cis-♀: 12 (0.89%), trans-♀: 21 (1.56%)
-```
-
-[TODO: wrap up]