Creating positive learning communities for diasporic indigenous students
Bibliographic record
Abstract
Diasporic Indigenous students include the lived realities of diverse Indigenous students living in the United States with familial, relational, and transnational ties to Indigenous communities and pueblos of origin in Abya Yala, also known as Latin America. In this article, we advocate for the creation of positive learning communities to best support diasporic Indigenous students in schools and beyond. Recommendations for educators include understanding the effects of anti-Indigenous discrimination within Latinx communities and reflecting on the ways schooling may unintentionally reproduce colonial or damage-centred perspectives about Indigenous Peoples. The successful cultivation of positive learning communities also requires schools to learn from and cultivate partnerships with diasporic Indigenous families and surrounding communities to uplift social-emotional learning that honours Indigenous comunalidad. We hope the information presented in this article contributes to promoting equitable learning outcomes for all students by disrupting colonial stereotypes and misinformation about Indigeneity and uplifting contemporary Indigenous saberes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".