Pathways Taken By One Canadian College to Advance Reconciliation and the Creation of a New Reconciliation Engagement Program with Indigenous Peoples
Bibliographic record
Abstract
Canada-wide efforts are being made to close the gaps that exist in the health and wellness of Indigenous Peoples besieged by a past of cultural genocide, oppression, and exploitation. The purpose of this essay is to provide members of Colleges and Institutes of Canada (CICan) access to a proposed program to engage in reconciliation, with the objective of facilitating Indigenous community engagement through social innovation, training, and applied research. The proposed program is exemplified through the relationship built between Collège Boréal and Dokis First Nation located in northern Ontario. The proposed Reconciliation Engagement Program consists of two streams that encourage CICan members to utilize, among other possible decolonizing methods, the tenets of a Critical Indigenous Methodology to value and foreground local Indigenous voices. The first stream would consist of networking activities to establish relationships, understand Chief and Council’s vision, and seek opportunities for capacity building within an Indigenous community. The second stream would be project-based so that capital costs and human resources can be accessed to complete each project. While proposing the new program is important, the present essay can also be used to exemplify how Canadian colleges and polytechnics can adopt a decolonizing approach during their engagement with Indigenous 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.074 | 0.015 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| 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".