Foucault, the “Facts,” and the Fiction of Neutrality: Neutrality in Librarianship and Peer Review
Bibliographic record
Abstract
This paper brings together two discourses in librarianship, that of neutrality in the context of library services, and that of peer review, which is of concern for librarianship as it moves more into the realm of scholarly communication. It points out the shortcomings of this ethical principle within the context of library services, using LIS literature on the opposition between neutrality and the commitment to social justice. It also uses Foucault’s theories on discipline, and knowledge and power, and Latour and Woolgar’s analysis of the construction of scientific facts, to critique the concept of neutrality. Then it asks how that critique applies to the practice of peer review, in which the expectation is that reviewers will be neutral or impartial judges of manuscripts. Findings suggest that the principle of neutrality, with a slightly different meaning in this context, does have useful applications to peer review, ensuring fairness. Although neutrality may never be possible completely, cross-disciplinary literature suggests ways to limit the effects of bias. Thus, librarians can better understand the different meanings of neutrality in these different contexts, including its usefulness and limitations.
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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 | 0.081 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.147 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".