Management, Appraisal and Preservation of Government Records: Some Aspects of the Canadian
Bibliographic record
Abstract
Over the last decades, archivists believed the management of government records, without being perfect, was going smoothly and seamlessly: records from Canadian government institutions were acquired, processed and made accessible to Canadians in an organised, structured, and logical way. However, in his 2014 report, the Auditor General of Canada pointed out not only weaknesses but also failures in LAC activities related directly to its mandate: developing Records Disposition Authorities (RDA), processing archival material from departments and agencies, and making this heritage material available to Canadians or people interested in Canada. LAC was somehow shocked by this earthquake and had to cope with these problems and find solutions, within three years. My presentation has three parts: 1- the LAC Act, macro-appraisal and RDA; 2- the Auditor General’s report; 3- the creation of the LAC Task Force to deal with these archival issues. My conclusion will explore some elements about the management of digital archives for Canadian government institutions.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.032 | 0.017 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".