Bibliographic record
Abstract
The Canadian Rules for Archival Description (RAD) standard is now just over twenty years old. How well has RAD fared? RAD took over the framework of then-existing bibliographic models for describing library items (AACR2, ISBD(G)) and adapted it for the description of bodies of archives. RAD’s successes are many and its impact on the Canadian archival profession and system profound. But the bibliographic framework has been abandoned elsewhere in the archival world, and librarians themselves have recently revised it; now we need to liberate RAD from it. The first section of the paper situates the development of RAD in the history of descriptive standards; the second discusses a number of problems with RAD and the difficulty of resolving them in the current framework. Comparisons are made throughout to the post-RAD descriptive standards, as well as to the 2004 effort (not finalized or implemented) to rewrite RAD as RAD2. The conclusion looks briefly at options for the future of the standard. The main proposal is that RAD needs a thorough revision that would more closely align it with international standards, enable it to better handle the descriptive challenges of digital objects, and accommodate the insights of recent critical writing on description that have expanded the notion of archival context.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".