Bibliographic record
Abstract
For nearly a century (1933 onwards), catalogers and others have engaged in discussions over the 'ethical' labeling of marginalized subjects in knowledge organization systems (KOS). In order to understand and contextualize contemporary conversations, I undertook a comprehensive review of this literature. The resulting project 1) synthesizes the broader history of these discussions, 2) examines its facets and subdomains, and 3) provides a foundation for the realignment of KO work towards social justice. To achieve these tasks, I replicated and expanded upon a now-unavailable database prepared by Hope A. Olson and Rose Schlegl in 1999. As this database suggests, the literature has expanded fivefold in the last two decades and taken a number of different directions. My analysis of these differences (here called KO 'subdomains') establishes a historiography of critical cataloging movements and a framework from which to understand them. It also demonstrates gaps in the literature, how contemporary authors have abandoned areas of early importance, and how certain subdomains have become nearly independent. Finally, my analysis indicates the insufficiency of a philosophical tradition descended from Ancient Greek Aristotelian “virtue” ethics as a method upon which to base twenty-first century KOS. Instead, I advance the concept of “equitable” knowledge organization and the realignment of KO work towards principals of social justice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.033 | 0.066 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".