Ethical Values in Archival Arrangement and Description: An Analysis of Professional Codes of Ethics
Bibliographic record
Abstract
The international literature on information science has devoted attention to ethical studies in information, especially due to the development of information technologies. However, the information organization activities have incipient ethical studies that are mostly focused on libraries. Thus, the area of archival science still lacks studies of this nature, which leads to question how the codes of ethics for archivists address issues related to ethical dilemmas of information organization activities, especially in core activities of arrangement and document description. Thus, this study aims to identify and analyze ethical values related to those aforementioned activities, by analyzing the codes of the following countries: Brazil, Portugal, France, Spain, Australia, Canada, USA, New Zealand, United Kingdom and Switzerland and the ICA codes of ethics. Applying a content analysis, the following values were found: access and use, authenticity, confidentiality, conservation, custody, impartiality, information access, information security, physical preservation of the record, reliability, respect for provenance, respect for the original order, respect for the preservation of the archival value of the record.
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 | 0.048 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".