Whose Classroom Is It? Unpacking Power and Privilege in University Women's Studies Classroom Spaces
Bibliographic record
Abstract
Women’s Studies students’ accounts of their experiences academically, emotionally and politically in feminist university classrooms will be investigated in this thesis. Central to my work, through an anti-racist feminist and intersectional analysis, is to demonstrate the ways in which Women’s Studies university classroom spaces are neither ‘innocent’ nor are they devoid of racism/white supremacy as it is present in the bodies who are allowed to enter the space, voices allowed to speak and knowledge being taught. As this research is informed by a personal experience in an undergraduate Women and Gender Studies course at a local university, I will use both auto-ethnography and interviews as method in and through anti-racist feminist research methodology. Highlighting the importance of anti-racism education as a call to action in attending to this disjuncture and also to erode superficial notions of sisterhood will demonstrate white feminist supremacy as an implication for the sociology of race.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".