Gender-based violence as difficult knowledge: pedagogies for rebalancing the masculine and the feminine
Bibliographic record
Abstract
Gender-based violence is a staggering but normalized global phenomenon, illustrated by the global reach of the #MeToo movement. Gender-based violence and the impacts of trauma enter learning spaces daily, acknowledged or not. Adult learners often respond to learning about gender relations with avoidance, denial, fear, defensiveness and trivialization, all facets of resistance. Britzman calls this ‘difficult knowledge’. Yet, education does reduce gender-based violence. The first step toward trauma-informed education is awareness of the pervasiveness of gender-based violence and its reverberations in education. Thus, we provide a global snapshot of statistics and definitions. Second, we describe an extensive literature review which revealed little explicit attention to gender-based violence in the field, reproducing hiddenness and ‘unspeakableness’. Third, we analyze the myths and theories about gender-based violence echoed by learners, which either reproduces the conditions of violence or creates opportunities for transformative learning. Drawing from masculinity and feminist studies, we analyze how genders are educated into patriarchy and violence, largely through informal education. We then propose principles for unlearning violence and trauma-informed education as well as guidance for addressing difficult knowledge and learner resistance. By unflinchingly addressing the deep structure of patriarchy, educators can design pedagogies for rebalancing the Masculine and the Feminine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".