Bibliographic record
Abstract
Abstract This book brings together a series of interrelated chapters on the ethics and politics of memory by the late feminist philosopher Sue Campbell. In Campbell's treatment of them, both memory and the self are deeply relational, the boundaries between individuals and collectives need to be interrogated, and there is a deep entanglement of epistemic and ethical norms. The chapters of Part I, diagnoses and responds to contemporary skepticism about personal memory and develops an account of good remembering that is better suited to contemporary (reconstructive) theories of memory. Being faithful to the past is both an epistemic and an ethical achievement, needing virtues of both accuracy and integrity, and often requiring us to re-negotiate the boundaries between individuals and collectives. The chapters of Part II concern the many activities and practices through which we explore and negotiate the shared significance of our different recollections of the past, and the importance of sharing memory for constituting our identities in relation to others. Views about self, identity, relation, and responsibility (all influenced by traditions in feminist philosophy) are explored through the lens of Campbell's relational conception of memory. The chapters of Part III, discuss Canada's Indian Residential Schools Truth and Reconciliation Commission, and use Campbell's relational conception of memory to illuminate and address the challenges of sharing memory, renewing selves, and transforming relationships in contexts fractured by moral and political difference, especially contexts rooted in past injustice and oppression.
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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 | 0.001 | 0.004 |
| 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 | 0.023 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".