"How do we look?": discourses of truth and reconciliation in select Manitoba school divisions
Bibliographic record
Abstract
Since the publication of the TRC’s Calls to Action, Manitoba school divisions must publish yearly Continuous Improvement Plans (CIPs) and Community Reports (CRs) as an accountability measure to report on progress toward reconciliation. Divisions, as organizational entities, use particular language in their documents to align with the goals of Truth and Reconciliation in order to maintain their reputation and relationships with education stakeholders. This study has one major research question: What discourses of Truth and Reconciliation appear to be constructed by Manitoba school divisions? Concepts, perspectives and methodologies from Indigenous scholarship, including ethical relationality and Indigenous Métissage, inform a Critical Discourse Analysis (CDA) of CIPs and CRs from school divisions from each of the five Treaty territories in Manitoba. Organizational Impression Management (OIM) is used as a theory to understand the motives of each division and how they wish to be perceived by the public. Themes emerging from the data include the prevalence of “achievement gap” discourse, individual vs. collective responsibility for facilitating education for reconciliation, and presenting reconciliation as foundational vs. additive in divisional priorities. Recommendations for practice include consistency in terminology, and a pedagogical and linguistic shift away from “achievement gaps” to “education debts.”
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.044 | 0.040 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".