Bibliographic record
Abstract
Appraisal and disposition of government records at Library and Archives Canada (LAC) focuses primarily on acquiring the “right” records to best document a given function of the Government of Canada. Once records pass into LAC’s care, access is provided through an inconsistent approach of online descriptive records and on-site finding aids, often with minimal or incorrect contextualizing information that hinders their overall discoverability and use. Through a study of both the legacy photographic records in the National Film Board of Canada Fonds and the recontextualization project currently underway at LAC, the author examines the history of the record, from recordkeeping practices to the transfer to LAC, and some of the interventions by the archives to describe and shape these records over several generations of custodial care. All of these various actions have had a hidden impact on the use and understanding of both the individual records and the larger collection. This article provides a case study in how rearrangement based on research into creators, organizational recordkeeping systems, and archival custodial practices can draw out complex, multiple provenances and provide researchers with a fuller contextual history of the record.
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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.028 | 0.024 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".