Bibliographic record
Abstract
In this paper, I argue that nonhuman animals can be subject to epistemic injustice. I consider Miranda Fricker’s account of the nature of the harm of epistemic injustice and highlight that it requires that a knower be invested in being recognized as a knower. I argue that a focus on know-how, rather than testimony or concepts for self-understanding and communication, can serve to highlight how nonhuman animals can suffer epistemic injustice without an investment in recognition, by focusing on distributive justice concerning epistemic goods. Drawing from work in animal ethology and movement ecology, I argue that human interruption of animal lifeways has negative downstream effects on nonhuman animals’ ability to acquire ‘answers’ to ‘questions’ they have an interest in answering: namely, acquiring both true beliefs about conspecifics and their environment, as well as acquisition of behaviors and skills that enable everyday successful coping, and that these interruptions can constitute epistemic injustice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".