“The Pandemic is Galvanizing Change”: Anti-Racism in Human Rights Education With Youth
Bibliographic record
Abstract
This article discusses how the heightened visibility of racial discrimination coupled with the repression of young people’s civil and political rights during the COVID-19 pandemic is surfacing the need for human rights education (HRE) to evolve to address anti-racism more intentionally. With youth’s amplified awareness of racism and their call for change, HRE practitioners reflect on the use of language, the limitations of “celebrating diversity” and the ways spaces are held for youth engagement as a means of building inclusion given the lived injustices across communities. As children’s rights researchers and practitioners, we consider how our focus on age has resulted in the inadvertent neglect of the interdependence of the rights to participation and to non-discrimination. Shifting to a more critical HRE includes embracing intersectionality and reflexivity, actively bringing BIPOC youth to the centre of sharing and informing, and cultivating youth engagement on racial justice to catalyze systemic-level change.
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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 | 0.024 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.039 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".