Toward <scp>Evidence‐Based</scp> Cataloging Ethics: Research, Practice and Training in Knowledge Organization
Bibliographic record
Abstract
ABSTRACT This panel considers the bridge between research and practice in cataloging ethics. Cataloging ethics – including indexing and classification – is an important part of practice, yet cataloging ethics research and practice are not always clearly connected. The purpose of this panel is to build towards more evidence‐based cataloging ethics practice. Two main areas will be considered. The Cataloging Code of Ethics (2021) is a vital part of these discussions: this major codification of cataloging ethics was the result of both practitioner input and much research. This panel will discuss ways in which the Code can lead to more research‐informed practices. Teaching and training is a crucial – and under‐discussed – aspect of cataloging ethics, both within library and information science education and workplace training. Therefore, the panel will contemplate how training and teaching can germinate research‐based practices. The panel will be in three parts: a panel presentation about cataloging ethics, including each member's perspectives and experiences on teaching and training in cataloging ethics; small group discussions about real world cataloging ethics scenarios, utilizing the Code to generate discussion; and feedback to the whole group with a closing discussion about strengthening the relationship between practice and research in cataloging ethics.
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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 | 0.252 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.037 | 0.025 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".