Redress and Reconciliation for Indigenous Peoples in the Form of Apologies: An Inadequate and Abysmal Procedure that Supports Settler Colonialism
Bibliographic record
Abstract
To reach redress and reconciliation with Indigenous Peoples, establishments such as the Catholic Church as well as the Canadian Government decided to issue “apologies;” however, these attempts are unauthentic as they support settler colonial ideals, and further promote the marginalization of Indigenous Peoples. This paper critiques current attempts of redress and reconciliation for Indigenous Peoples by contending these “apologies” are insincere. Drawing on various frameworks provided by scholars such as Borrows, Palmater, Corntassel and Holder, as well as Tavuchis and James, this paper analyzes apologetic attempts made by the Catholic Church, former Canadian Prime Minister, Stephen Harper (2008), and Pope Francis (2022) to argue that “apologies” are not only inadequate forms of reconciliation, but also insinuate absolute disregard and disrespect towards all Indigenous Peoples. Most importantly, this paper claims that the Canadian Federal Government must implement strategies of reconciliation with Indigenous Peoples by including them in policy-making decisions.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.056 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".