Bibliographic record
Abstract
Political scientists have recently noted that we live in an "age of apologies", in which certain governments apologize for certain kinds of historical wrongs.This has been markedly true in Canada, with at least 10 major apologies since the late 1980s.The first of these, and one to which I give particular attention in the paper, is the apology to Japanese Canadians, for their internment during World War II, but there have also been apologies for the legacy of residential schools in Indigenous communities, for the forced relocation of Inuit to the High Arctic, for the head tax leveled on Chinese immigrants to Canada, for the razing of Africville, a predominantly Black neighborhood in Nova Scotia, and more.While these apologies are often considered separately, and in political terms, as movements towards national inclusion (for racialized or ethnic groups) or national sovereignty (for indigenous ones)), in this paper I argue that it is important to consider them together.To do so helps illuminate some of the political economic dynamics shaping the rise in apologies and why and how this rise in apologies is co-extensive with significant neoliberal transformations in the Canadian state, and of Canadian ways of understanding diversity.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".