Bringing Order to Chaos Ethically: “Cataloging Code of Ethics” and Critical Cataloging
Bibliographic record
Abstract
Goal: The goal of this paper is to address the ethics in cataloging and the concept of critical cataloging, as a sub-movement of critical librarianship, as well as to highlight the intersection of ethics with critical in cataloging. It also provides an overview of the most relevant academic papers, articles, and documents in this field, with a special focus on the Cataloging Code of Ethics, the first comprehensive, collaborative, and internationally accepted Code specific to the profession. This code was published in 2021 by the Cataloging Ethics Steering Committee (CESC), which includes representatives from three library associations from three different countries: the American Library Association (ALA), the Chartered Institute of Library and Information Professionals (CILIP), and the Canadian Federation of Library Associations (CFLA-FCAB). Approach/methodology/design: Using content analysis, this paper presents relevant research on ethical cataloging. Through the methods of comparison, analysis, and synthesis, an inductive approach is used to establish a theoretical framework for the subject of the research. Results: The results show that, particularly in the last two decades, this topic has gained significant attention within the cataloging community. Catalogers, aware of their role in building information architecture, have begun to critically reflect on their profession and articulate the need for a document that provides guidelines for ethical organization of metadata. Social relevance: The research advocates that the power to organize knowledge and the power to name, held by catalogers, must be balanced with professional ethics. A critical approach to the profession and the Cataloging Code of Ethics can aid in this process, which could have positive implications for society as a whole. Originality/value: The originality of this paper lies in its critical examination of a cataloging trend that has not yet been discussed in our region.
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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 | 0.056 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.016 | 0.149 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".