Bibliographic record
Abstract
This paper begins by discussing Sue Campbell's account of memory as she first developed it in Relational Remembering: Rethinking the Memory Wars and applied it to the context of the false memory debates. In more recent work, Campbell was working on expanding her account of relational remembering from an analysis of personal rememberings to activities of public rememberings in contexts of historic harms and, specifically, harms to Aboriginals and their communities in Canada. The goal of this paper is to draw out the moral and political implications of Campbell's account of relational remembering and thereby to extend its reach and application. As applied to Aboriginal communities, Campbell's account of relational remembering confirms but also explains the important role that Canada's Indian Residential Schools Truth and Reconciliation Commission (IRS TRC) is poised to play. It holds this promise and potential, however, only if all Canadians, Aboriginal and non‐Aboriginal, engage in a process of remembering that is relational and has the goal of building and rebuilding relationships. The paper ends by drawing attention to what relational remembering can teach us about oppression more generally.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".