We-Relation: Narratives of Emergence, Education and Resistance
Bibliographic record
Abstract
The educational landscape and curriculum are shifting tremendously as educators attempt to grapple with systemic and global issues, growing divisions, humanitarian crises, institutional and political violence, racial and religious injustices, and righteous and dangerous resistance which are surfacing during these intense times brought forth by the COVID-19 pandemic. Presently, educators face many frustrations and disappointments whereby working to create needed change becomes inevitable. In this position paper Momina and Irteqa as mother and daughter, Canadian Muslim women of South Asian ethnic decent, writers, poets, and critical scholars, share the truth of our knowing as an alternative way of knowing and initiating dialogue. By breaking the silence on oppressive systems and ideologies and reimagining and renegotiating curriculum, pedagogy, histories, and epistemologies from racialized perspectives we can transcend our collective suffering.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".