“He/his/she/her/father/mother/son/daughter”: A critical reflection of reproductions of cis-normativity and cis-dominance in preparing qualitative data for analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this research note, we present a critical moment we had as a research team in our work preparing qualitative data for its analysis in which an unanticipated social justice issue was triggered. The moment was related to determining the best ways to anonymize the information—in particular, how to replace what were perceived as “male” and “female” gendered names, family relationships, and roles with the labels of “she/her/mother/daughter” or “he/his/father/son”. The paper begins with a review of the main “do’s and don’ts” of data preparation, followed by our reflections of the social justice issue. Through our differently positioned reflections, we complicate the task of data preparation by revealing the ways in which cis-dominance is upheld by cis-normativity, cis-genderism, and heteronormativity. We end with recommendations for practices that uphold the values and goals of social justice by resisting cis-dominance and challenging the erasures of peoples with fluid genders and identities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.160 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it