Embodying Sexism: A 'DIY Project' Exploring Feminism & Teaching in the #MeToo Era
Bibliographic record
Abstract
We are in the midst of a watershed moment as the # MeToo movement continues to carve new spaces for women’s voices and lived experience of sexism, abuse, and harassment to be heard. Schools are not immune from this cultural moment, with sexual harassment identified as a major concern for both students and teachers (Higham, 2017). Hodgson, Vlieghe, & Zamojski (2018) call for the need to develop a post-critical philosophy through which to approach such concerns, stating “we always can begin anew with the world” (p. 8). Highlighting the imminent need for educators to carefully consider the pedagogical implications of this cultural moment, embarking upon ‘the precarious adventure of transforming and recreating the world’ (Freire, 1972, p. 72) as they develop ethical strategies for working with students, and each other, amid this complexity. Ahmed (2017) reminds us, “to direct your attention to the experience of being wronged can mean feeling wronged all over again. We need to attend to the bumps; it is bumpy” (p. 27). Utilizing the arts-based practices of photography & poetic inquiry, this paper engages in an autoethnographic exploration seeking to attend to these bumps and their impact on my identity as teacher, researcher, and feminist.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.034 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".