The Feminist XResistance: Project Reflections and Commentary
Bibliographic record
Abstract
On May 31, 2023, we showcased the Feminist XResistance project at the Women and Gender Studies et Recherches Féministes (WGSRF) conference under the apt thematic “Take Back the Future.” The project started on July 9, 2022, when a group of international, interdisciplinary, early career feminist scholars convened on Zoom for the Feminist Digital Methods (FDM) Drop-in Virtual Lab hosted by York University’s Centre for Feminist Research (CFR). The drop-in took place two weeks after the United States Supreme Court overturned the constitutional right to an abortion and became a digital space to express our fears and anger over rising gender essentialist fascism, worries about the future, and to imagine feminist digital methods for resistance. In this reflection and commentary, we share our observations and processes for the Feminist XResistance project, starting with our first exploratory workshop, our co-creative analysis and outputs, the development of our AR installation, and, finally, our conclusions and insights.
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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 | 0.045 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.036 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".