Exploring the Evolution of Gender Identity through Subject Pronouns: A Comparative Study of Gender Equality Communities
Bibliographic record
Abstract
This study aims to explore the use of subject pronouns in the context of gender identity in gender equality communities at the national and international levels. A qualitative approach with a comparative case study design was chosen to explore individual experiences, perceptions, and views regarding the importance of using inclusive pronouns. This study involved two research locations, namely Indonesia and several international countries such as the United States, the United Kingdom, and Canada. Research subjects were selected through purposive sampling, with members of the gender equality community involved in campaigns or policies related to gender identity. Data were collected through in-depth interviews, participatory observation, document analysis, and focus group discussions (FGDs). The results showed that although the use of inclusive pronouns has been widely accepted in international countries, major challenges in changing old habits are still found in Indonesia, where the habit of using binary pronouns is still dominant. This study concludes that changes in pronoun use require continuous educational efforts and policy support to accelerate the acceptance of inclusive pronoun use in society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".