Bibliographic record
Abstract
It might be argued that arrangement and description is something of a poor child in the competition for attention in these pages.Although the journal has published several articles on arrangement and description over the years, this issue is the first one to be wholly devoted to the subject.There have been two special issues on electronic records.Other issues in recent years have paid particular attention to such subjects as the history of records and archives, archives and culture, archives and photography, queer archives, archives and the law, and archives: space and power.The archival function of appraisal, which one suspects is widely regarded (falsely, I think) as more intellectually challenging than the function of identifying and representing archives, has regularly been the subject of articles in recent years.This relative neglect of arrangement and description is unfortunate, for many of the threads of thinking about archives in the digital age and about connecting archival science with wider currents of thought in other disciplines ought to be brought to bear on consideration of arrangement and description, which is what this issue aims to do.We open with Richard Dancy's searching examination of the birth, life, and shortcomings of the Canadian Rules for Archival Description (RAD).He begins with the history of the making of RAD and efforts to reform it, all in the light of international developments.He then judiciously and persuasively presents the case that the existing structure of RAD inhibits making necessary reforms, offers a wide-ranging analysis of where rules are lacking, and canvasses three possible ways to make needed improvements.In his conclusion, he observes that "more than anything else, what is needed now is a period of debate in which the Canadian archival community begins to look critically at its standard.Part of the difficulty RAD2 faced was that it provided answers to questions very few Canadian archivists were asking.RAD is a community standard: it does not need fixing if the community does not perceive anything wrong with it."It is difficult to believe that discerning readers of his article will think there is nothing wrong with RAD, but easy to believe that realizing reform necessitates overcoming some serious hurdles.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".