9 Matching Annotations
  1. Mar 2017
    1. we observe that women’s acceptance rates are higher than men’s for almost everyprogramming language

      Through the chi-squared test which is a statistical method assessing the fit between observed values and those expected theoretically

    2. if we look only at acceptance rates of pull requests thatmake changes to program code, women’s high acceptance rates might disappear.

      Reason for skewed data.

    3. the women whoremain and do the majority of pull requests would be better equipped to contribute, and defendtheir contributions, than men.

      Seems like a lot of assumptions are being made.

    4. One plausible explanation is that women’s first few pull requests get rejected at a disproportion-ate rate compared to men’s, so they feel dejected and do not make future pull requests

      Here the authors are saying that the original claim they made could have been devised due to this qualitative reasoning

    5. Perhaps our GitHub data are not representative of the open sourcecommunity

      after the findings have displayed the reverse claim, the authors of the article suspected that the data on GitHub simply did not represent the community well enough

    6. women tend to have resumes lessfavorably evaluated than men

      supports the idea that pull requests made by women are accepted less often

    7. The hypothesis is not only false, but it is in the opposite direction than expected;womentend to have their pull requests accepted at a higher rate than men

      the non-peer reviewed article, written by three colleagues at the North Carolina State University, disproves the claim that pull requests made by women are less likely to be accepted than men's