Creating New Stories: The Role of Evaluation in Truth and Reconciliation
Bibliographic record
Abstract
Abstract: This paper describes the origins of the Truth and Reconciliation Commission of Canada, with the focus on how evaluators and their professional associations can contribute to truth and reconciliation. At the professional association level, the actions that the Canadian Evaluation Society has taken in committing itself to incorporating truth and reconciliation into its values, principles, and practices are highlighted. At the individual level, evaluators are challenged to reflect on their practice. As storytellers, evaluators have been complicit in telling stories that, while highlighting the damaging legacy of residential schools, have had little influence on changing the status quo for Indigenous peoples and communities. The need to reconsider who should be telling the stories and what stories should be told are critical issues upon which evaluators must reflect. The way forward also needs to include a move toward a more holistic view, incorporating the interaction between human and natural systems, thus better reflecting an Indigenous, rather than a Western, worldview. The imperative for evaluators, both in Canada and globally, to see Indigenous peoples “as creators of their own destinies and experts in their own realities” is essential if evaluation is to become “a source of enrichment … and not a source of depletion or denigration.”
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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 | 0.167 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.027 | 0.077 |
| Scholarly communication | 0.046 | 0.023 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".