Reconciliation Framework: Response to the Report of the Truth and Reconciliation Commission Taskforce
Bibliographic record
Abstract
Released in February 2022, the Reconciliation Framework is designed for non-Indigenous archivists in Canada who manage Indigenous holdings in their repositories, from acquisitions to outreach and all processes in-between. The document positions itself well amongst other related international standards that advocate for a reciprocal, ongoing relationship between archival institutions and the Indigenous communities they purport to represent and serve. The journey to final publication reaches back not only years and decades but also centuries, considering it was borne out of the aftermath of the terrible history of residential schools in North America. Recent formal calls to action demanded redress through equal parts respect, relevance, reciprocity, and responsibility. This new framework provides archivists with additional tools to begin difficult conversations and engage in hard (but rewarding) work of participating in this critical reconciliation process.
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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 | 0.171 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.033 | 0.029 |
| Scholarly communication | 0.043 | 0.024 |
| Open science | 0.015 | 0.032 |
| Research integrity | 0.086 | 0.067 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".