Conceptual Amelioration as a Response to Assumptions of Neutral Descriptive Language
Bibliographic record
Abstract
Libraries are reliant on knowledge organization systems such as the Library of Congress Subject Headings to make information accessible to users. This article draws on writing in critical librarianship to highlight areas, particularly involving the description of people, where the language of classification can perpetuate harm, dehumanization, and misunderstanding. It also charts progress and criticism of the way we classify gender and sexuality, disability, race, and immigration status. The paper uses the work of analytic philosophers Sally Haslanger and Ludwig Wittgenstein to highlight the problems of treating classification terms as neutral descriptive language rather than inherently political and social concepts. Haslanger’s project of ‘conceptual amelioration’ is used to frame the ways libraries can and should acknowledge that the way language is organized has real-world impacts on the way members of a community coordinate, or interact, with each other. Identifying the ways we privilege assumptions from the dominant white, settler, heteronormative mainstream culture and recognizing them as a trained set of symbols can help us make localized decisions about language that are better for our communities of learning, our colleagues, and our library users.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.124 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.159 |
| Scholarly communication | 0.018 | 0.037 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.009 | 0.027 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".