Gender and sexuality in knowledge organization systems
Bibliographic record
Abstract
This article addresses the terms and concepts in knowledge organization systems related to individuals and social groups with sexual and gender identities that do not conform to cis-heteronormativity. Preliminary insights suggest that these concepts are not represented adequately in knowledge organization systems. Although advancements in gender and sexuality studies have occurred, these systems have not developed accordingly and as a result, hinder the recognition of the diverse range of sexual and gender identities thereby risking the perpetuation of prejudiced and discriminatory discourses about the respective individuals and groups. This exploratory qualitative study investigates terms and concepts reflecting terminological, social, and rights advancements relating to sexual and gender-diverse individuals. A bibliographic search was conducted in books, scientific articles, guides, and manuals addressing these advancements to support the construction of a conceptual map representation of the concepts. The study systematically structured representations hierarchically, beginning with overarching concepts and gradually delving into more specific ones. This approach was guided by a comprehensive understanding of the domain under analysis, ensuring clarity and accessibility for the reader. The organization of knowledge needs to incorporate gender and sexuality in the construction of knowledge organization systems so that diversity is adequately represented in these systems.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".