The Grammar of Social Justice: Gender Non-Binary Pronouns and the Writing Centre
Bibliographic record
Abstract
This paper discusses our writing centre’s outreach to trans and gender non-conforming students on our campus and the subsequent responses to this. Specifically, our writing centre embarked on an outreach campaign through promotional materials and sponsored events. During and following the 2016 U.S. presidential election, we found our outreach posters repeatedly defaced. We present a case study of this situation, beginning with background information about our campus and a review of events. In order to think through the potential for a transformative response to these events, we explore the history and reasoning behind the use of gender non-binary pronouns; Michel Foucault’s theory of discourse and power; and José Esteban Muñoz’s theory of disidentification. Lastly, we present what we have done since late 2016 and what we recommend as further steps towards a transformative response.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".