Remembering Together: Examining Epistemic Injustice Through the Lens of Relational Remembering
Bibliographic record
Abstract
I argue that causes of epistemic injustice as well as the project of working towards epistemic justice can be understood through the lens of relational remembering.In Chapter 1, I offer a brief overview of the project.In Chapter 2, I discuss Sue Campbell's account of relational remembering, which holds that good remembering aims to get something correct about the meaning of the past.In Chapter 3, I examine Miranda Fricker's formulation of epistemic injustice as a uniquely epistemic form of injustice that occurs in relation to a subject's status as a knower.I then build on critiques of Fricker by José Medina and Gaile Pohlhaus Jr. to highlight relational features.Chapter 4 expands on the preceding chapters, drawing them together to argue that conditions for epistemic injustice are created through the failures of the epistemically privileged to correctly discern the meaning of the past.In Chapter 5, I argue that practices of good remembering can disorient the epistemically privileged in ways that can generate new awareness of relationships of oppression and injustice as well as create the space to begin cultivating epistemic virtue.Chapter 6 offers a summary of the arguments that this thesis puts forth as well as a brief discussion of what can be developed going forward.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".