Bibliographic record
Abstract
There is a rubric for enacting anti-discriminatory education to be found in the combined theoretical areas of culturally sustaining and trauma-informed education. While each theory on its own can inform a teaching practice rooted in anti-oppressive methods, the combination of the two is a loving step forward for educators interested in disrupting colonial legacies of Eurocentric domination and whiteness in academia, as well as the neoliberal disembodiment required by a capitalist institution striving for marketability. Where culturally sustaining pedagogy argues for a model of teaching which decenters whiteness, recognizes the fluidity of cultural subjectivity and the credibility of non-Eurocentric knowledge, trauma-informed pedagogy asks us as educators and learners to be cognizant of the deeply pervasive nature of different forms of trauma. Students with a trauma history are often the same students who are culturally marginalized and in a state of cultural and emotional disconnection due to the subjugation of their subjectivities in Westernized academic spaces. In this chapter, I summarize some background literature on culturally sustaining and trauma-informed pedagogies, before moving into specific suggestions for a framework of anti-discriminatory pedagogy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".