Changing Tides: A Critical Reflection on Neutrality and Antiracism in LIS
Bibliographic record
Abstract
Neutrality in librarianship is a nonsensical concept that libraries of all kinds (e.g., public, academic, special, etc.) should not strive to embody in any shape or form. The following paper investigates the intricate relationships between neutrality and racism as they are demonstrated by tangible and intangible forms in library and information science (LIS) spaces. Ian Williams’ book chapter, “More Than Half of Americans Can’t Swim”, from his book, Disorientation: Being Black in the World (2021), is utilized to critically reflect on Blackness as identity and some ways in which antiracist practices clash with neutrality within LIS spaces. I conclude with urgent reminders of how detrimental neutrality can be if it is not thoroughly rooted out and supplanted with antiracist practices that the LIS field is in dire need of.
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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 | 0.050 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.043 | 0.127 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.015 | 0.040 |
| 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".