The Changing Face of Archives: Will You Recognize Us? Part II
Bibliographic record
Abstract
Progressively since the 1980’s the main training and hiring route for archivists is\nthrough the various titled Masters in Archival Studies programs, in English at U\nof T, Manitoba, and UBC. The entrants to these programs have a far more diverse\nbackground than the vast majority of entrants to the archival field prior to 1985,\nand now constitute a significant portion of the archival profession. Remember in\npart this is because few archives have hired and those that have, are nearer the\nlocations of these schools. Prior to the archives schools, the majority of people\nworking in archives had some sort of academic history background, often at the Masters level or higher.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".