“There’s no word in my language for reconciliation”
Bibliographic record
Abstract
The purpose of this conceptual article is to evidence the emergence of the discourse of reconciliation in the last two decades with an aim to identify Canadians’ under-developed understanding and application of reconciliation and to critically interrogate the way in which the concept has been appropriated and applied by governments, organizations, and individuals. The many “faces” of reconciliation include political reconciliation as understood outside of the TRC, truth and reconciliation as reflective of the TRC, and institutional reconciliation (or co-optative and performative applications). The opaqueness around these differing movements leads to easy co-optation of reconciliation within colonial institutions, limits the transformational opportunities within the broader reconciliation movement, and contributes to stagnation and collective malaise towards reconciliation to the detriment of broader settler colonial decolonialism. Our methodology includes a review of existing literature and selected interviews with Indigenous language speakers who discuss a range of understandings and applications of reconciliation within Anishinaabemowin and Michif. Inspired by these new conceptions, the authors argue in an original contribution to scholarship that reconciliation can only be a useful narrative if it is anchored, through language, in Indigenous understandings of justice. The social impact of this work includes understanding that the ways in which reconciliation is mobilized in Canada is important and understanding how Indigenous language, instead, may offer key cultural insights and understandings for what it means to address wrongs.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.083 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".