Bibliographic record
Abstract
This paper compares the 2012 International Federation of Library Associations and Institutions’ Code of Ethics for Librarians and Other Information Workers and the 2013 Data Science Association’s Data Science Code of Professional Conduct and discusses the disjuncture and related considerations that might strengthen practical understandings of the implications of ethics in library and information professional practice. This paper cautions against conflating a data scientist’s ethical framework with those of the traditional librarian and supports the development of a more robust framework for library and information ethics and a more comprehensive and inclusive framework for thinking about and conceptualizing data ethics.Ce document compare le Code de déontologie des bibliothécaires et des autres professionnels de l'information de 2012 de la Fédération internationale des associations de bibliothécaires et d'autres institutions, et le Code de déontologie des données scientifiques 2013 de la Data Science Association, et discute de la disjonction et des considérations connexes, l'éthique dans la pratique professionnelle des bibliothèques et de l'information. Cet article déconseille de confondre le cadre éthique d'un data scientist avec celui d’un bibliothécaire traditionnel et soutient le développement d'un cadre plus solide pour l'éthique des bibliothèques et de l'information et un cadre plus complet et inclusif pour penser et conceptualiser l'éthique des données.
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 | 0.060 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".