Canada’s Arctic Policies & Truth and Reconciliation: An Examination of Canada’s Arctic and Northern Policy Framework through a Reconciliation Lens
Bibliographic record
Abstract
In September 2019, the Canadian Government launched Canada’s Arctic and Northern Policy Framework. One of the main goals of the framework is to achieve reconciliation with Indigenous Peoples by way of taking a co-development approach. But what does reconciliation look like exactly? And how are we to know whether the federal government is meeting the objective of reconciliation in the development of this framework? Since the release of the Final Report of the Truth and Reconciliation Commission of Canada in December 2015, a number of scholars have written about the question of how to attain reconciliation. One scholar in particular, Deborah McGregor, an Anishinaabe scholar from Whitefish River First Nation, Birch Island, Ontario, proposes six suggestions from which to assess whether reconciliation processes have been implemented in post-secondary institutions. McGregor concludes that these suggestions, while not exhaustive, represent a place from which to begin dialogue about establishing reconciliatory processes within the institution. Using McGregor’s suggestions, this article examines whether the federal government has implemented reconciliatory processes in the development of Canada’s Arctic and Northern Policy Framework.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.029 | 0.029 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.004 |
| 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".