Truth and Reconciliation Commission Gives Municipalities the Opportunity to Defy their Status as "Creatures of the Province"
Bibliographic record
Abstract
This paper analyses the Truth and Reconciliation Commission’s (TRC) inclusion, or lack thereof, of Municipalities as a critical power structure in the process of decolonization and resurgence across settler-colonial Canada. The TRC only called upon Municipalities five times, suggesting that they have little importance to the process. This indicates that it is up to the Federal and Provincial governments to address the calls to action, to which they would only be able to apply a top-down and “one size fits all” approach. This approach is insufficient because many diverse subgroups of Indigenous people live across Canada. So, who will effectively help develop policies, resources, and urban planning for the local Indigenous communities? That would have to be municipalities, unlike what the TRC is suggesting. Municipalities are more capable of providing a grass-roots approach to urban planning and policymaking when addressing the calls to action, something the other levels of government can not do, therefore, defying the ideology of being known as “Creatures of the Province” and signifying their importance to the process. Throughout this paper, I identify how municipalities can effectively take on the calls to action by recognizing and including the unique Indigenous identities within urban spaces, a requirement in accomplishing true reconciliation, decolonization, and Indigenous resurgence.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.029 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".