Bibliographic record
Abstract
Based on historical and theoretical reflections it is argued that speculation cannot be eradicated from psychology and that it is a necessary part of empirical research, specifically when it concerns the interpretation of data. The quality of those interpretative speculations of data is particularly relevant when they concern human groups and differences between them. The term epistemological violence (EV) is introduced in order to identify interpretations that construct the `Other' as problematic or inferior, with implicit or explicit negative consequences for the `Other,' even when empirical results allow for meaningful, equally compelling, alternative interpretations. These interpretations of data are presented as `knowledge' when, in fact, harm is inflicted through them. Examples of EV in the context of `race' are briefly discussed. The concept of EV also demonstrates that the traditional separation of `is' and `ought' is problematic. Reflections on epistemological-ethical issues are provided.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.195 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.015 |
| 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".