Conflicts of neutrality: Exploring definitions, values, and practices among Canadian academic librarians
Bibliographic record
Abstract
Library neutrality, often considered a core value of librarianship, has been facing growing opposition in recent years, but little research exists on how it is being defined and prioritized by practicing librarians. Normally more of a concern in public libraries, increased politicization of academic spaces is bringing the neutrality debate to college and university libraries . This article presents the results of a survey of Canadian academic librarians' attitudes towards library neutrality, including how they define, value and practice neutrality. It is found that Canadian academic librarians most commonly define neutrality as “not taking a side” and that ambivalent and negative conceptions of neutrality are prevalent. Neutrality is largely considered to be impossible and unethical, and seen as significantly less valuable than other library values such as access to information and social responsibility. The unfavourable conceptions and low value attached to neutrality are reflected in Canadian academic librarians' actions and practice. Many librarians are purposely contravening the principle of neutrality by acting in ways that they consider non-neutral, with social justice is a frequent impetus for non-neutral action.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.038 | 0.028 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".