Bibliographic record
Abstract
The Library of Congress Subject Headings is a standardized system for indicating the topics of works held in the library. Since LCSH is built using literary warrant, based on the terminology and proportions of published works, it is appropriate to assess it according to how accurately its terms match the works it is applied to. This creator-centric analysis focuses on areas in which LCSH is more likely to fail, given previous critiques of its biases and outdated language. In a pair of interview studies, creators shared their assessments of the subject cataloguing of their works. One study used works about Indigenous Peoples and the other used items from an LGBT2QIA+ community library. I analyzed the interview transcripts to identify different kinds of objections creators made to the subject cataloguing and how they made these objections. Creators objected to terms included in the records when the terms used were the wrong level of specificity, a poor match to the language used in their works and in their scholarly or creative fields, or misled the reader as to the content of their work. Creators objected to omissions from subject indexing when they expected particular terms to be used by their peers and readers, when they expected the genre or approach to be listed in the record, and when the record left out colonial actors or conflict. Applied to a system built on the principle of literary warrant, the objections from creators reveal major flaws in the term list and its application to published works. The types of errors noted suggest a need for better resourced cataloguing work, including professional development, more time to produce a record, support for nominating new terms, and capacity for local subject indexing and vocabulary management. Other types of errors reveal different understandings on the purpose and scope of subject indexing and imply areas for greater library outreach and transparency.
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 | 0.041 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".