Records of Dubious Research Value: Developing and Implementing Acquisition Policy for the Records of Non-Profit Organizations at Library and Archives Canada
Bibliographic record
Abstract
1 I use the current name of the institution for simplicity in the article although for most of the period covered it was the National Archives of Canada.Quotations and citations use the form of the name used in the original.Note that statistics for the pre-LAC period do not include figures for the National Library of Canada. 2 National Archives of Canada, Private Sector Acquisition: Orientation 1995-2000 (20 November 1995); and National Archives of Canada, 1995-96 Estimates, Part III, Expenditure Plan (Ottawa, 1995).3 Private-sector records creators include corporate bodies and individuals.Corporate bodies include both for-profit businesses and non-profit organizations but it is this latter group with which this paper is primarily concerned.Business archives are excluded from this study although they suffered a fate perhaps even more severe than that of non-profit organizations.
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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 | 0.080 | 0.212 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".