Collective Amnesia as an Epistemic Injustice
Bibliographic record
Abstract
Abstract By considering real-life cases of epistemic reparations (Lackey 2022), such as the Truth and Reconciliation Commissions in Canada, I identify and characterize a form of epistemic injustice that I call “collective amnesia.” I distinguish this phenomenon from other recognized forms of epistemic injustice and argue that collective amnesia specifically leads to primary and secondary epistemic harms in the form of distorted representations of a community’s past, preventing an even broader epistemic community from gaining adequate knowledge of its past and present identities. More precisely, I argue that collective amnesia arises as an interplay of negative hermeneutical injustices, whereby conceptual tools are lacking (Fricker, 2007), and “positive” hermeneutical injustices, whereby the positive presence of distorting and oppressive concepts defeats or prevents the application of more adequate concepts or narratives (Falbo, 2022). In addition, I address and respond to four objections. The first two objections allow me to identify two necessary conditions under which instances of collective forgetting are morally relevant and thus may count as instances of collective amnesia as an epistemic injustice: they must be partly agential, whether on the part of individuals or structures, and due to hermeneutical marginalization. The last two objections enable me to precisely define the scope of this epistemic injustice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".