On Working Toward Equitable Learning Communities: Thinking Across National Borders
Bibliographic record
Abstract
Abstract Through duoethnography, the lens of Critical Race Theory, and the concept of brave space, White Canadian and Diasporic African (African American) cousins and scholars consider the possibilities of reshaping classroom realities in a good way through expanding traditional theoretical approaches to incorporate marginalized voices. School-university-community research incorporating a wider range of theoretical approaches can amplify Indigenous and globally-inflected voices and resources in classrooms from Kindergarten through postsecondary. Decolonizing education and educational research through place-based, contextually- and culturally-responsive, connected learning communities developed through dialogic brave space can contribute to closing educational equity gaps. In our experience, supporting students with pathways to multilingualism and multicultural understanding can provide bridges to cross-cultural communication and understanding. The forms of ontological, epistemological, and linguistic diversity that are included in educational spaces affect the culture of those spaces. Further school-university-community dialog and research is needed to provide culturally-relevant, practical steps to create equitable learning communities.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".