Reimagining Educational Success: Lessons on Support, Wellbeing, and Trust from Community-Grounded Research with Black Families and Gender-Diverse Youth
Bibliographic record
Abstract
We present a dialogue between two community-based scholars in Tio’tia:ke/Montréal, who are examining the experiences of low-income Black families and youth, and gender-diverse, homeless youth. We argue that success must be understood differently in light of the systemic discrimination many youth navigate in schools and explore how research may mirror experiences of discrimination and lack of access that youth navigate in schools. The article highlights how relational research approaches may provide lessons for supporting youth and community leadership and posits that we must foster deep practices of trust-building, shared aims for research impact, and trust in youth.
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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 | 0.103 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.029 | 0.070 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".