âNew Contexts of Permanent Changeâ in Digital Archivy
Bibliographic record
Abstract
It has been eighteen years since Archivaria published a special issue on electronic records.In the introduction to the special issue (Archivaria 36 -Autumn 1993), Roy Schaeffer stated that the 1987 conference of the Association of Canadian Archivists was the first national conference within Canada devoted to electronic records and automated archival techniques.However, Schaeffer noted that we should not feel too comfortable regard ing our sense of achievement in the intervening six years, as "many chal lenges [regarding electronic records] identified in 1987 remain unaddressed." 1 Schaeffer also alerted the reader to the potentially troubling fact that "there are more articles dedicated to the subject of automation here [in Archivaria 36] than appeared in all of the issues [of Archivaria] produced between 1976 and 1987." 2 Of course, the history of electronic records in Canada -and beyond -goes back earlier than 1976.Of particular note is Michael E. Carroll's case study of the Public Archives of Canada's (PAC) implementation of a "machine-read able archives" program, presented as a paper at the 1974 International Council on Archives Conference on Archives and Automation, and subsequently published in The Canadian Archivist.3 Betsey Baldwin's article in Archivaria 62 provides an excellent overview of the issues and controversies regarding electronic records and automation both within Canada -particularly at the PAC -and within the United States.4 Personally, however, I feel that one of the greatest influences on organizing this special issue has been a 1972 article by Hugh Taylor.5 While not specifically about electronic records, Taylor provides
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.072 | 0.185 |
| Scholarly communication | 0.044 | 0.019 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".