“Now Is the Time to Start Reconciliation, and We Are the People to Do So”, Walking the Path of an Anti-Racist White Ally
Bibliographic record
Abstract
Media accounts of hundreds of unmarked graves of children at the sites of residential schools in Canada in 2021 is one more urgent call for all Canadians to start walking the path for reconciliation, decolonization, and anti-racism. In this exploratory reflection utilizing hermeneutical phenomenology, my journey to reconciliation is described. Through a review of Indigenous law and sovereignty, Canadian numbered treaties, and residential schools, this article explores justice, discovering the truth, and advancing reconciliation. In order to achieve justice, first ethnocentrism, or our evaluation of Indigenous cultures according to our preconceived preference for our own standards and customs, must be recognized, exposed, and set aside. Without our own ethnocentric attachment, and consequently with an open mind, we can hear the truth of Indigenous peoples and internalize it. Examples include the truth of the treaties and residential schools. The reconciliation path entails pursuing justice; this includes recognizing both Indigenous sovereignty and Indigenous law. This path doesn’t ‘restore’ relations historically, but does build reconciliation for the future. However, the process will not be comfortable. The reward will be a more equitable and inclusive society.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.050 | 0.080 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".