How Soon Is Now? Writings on Digital Archiving in Canada from the 1980s to 2011
Bibliographic record
Abstract
Abstract During the 1980s and 1990s, electronic records were a source of general concern and debate among Canadian archivists. There was a heightened interest in, and increasing numbers of articles about, electronic recordkeeping projects such as the University of British Columbia (UBC) Project and the National Archives of Canada's Information Management and Office Systems Advancement (IMOSA) Project, as well as non-Canadian projects like the Pittsburgh Project. Such projects often represented a turning away from approaches established and expertise gained during the machine readable archives (MRA) era. Nonetheless, digital archival work outside of these projects continued, much of it moving in trajectories set during the MRA era. This article suggests that MRA methods, ideas, and approaches have reemerged today as foundational to contemporary digital archives theory and practice.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".