Two Examples of Cooperation between Libraries and Archives (Powerpoint)
Bibliographic record
Abstract
For some ten years the two professions have been engaged in in-depth reflection on the content of their missions in an environment transformed by the Internet and driven by the digital world. The place of libraries in society has indeed considerably increased while the audiences for archives broadened and diversified. A consensus was reached about the concepts of a one-stop service and of local, national and international networks. This implies an active convergence between libraries, archives, museums and other cultural institutions. Canada and Quebec chose to merge their institutions in the field: Libraries and Archives Canada in 2004 and Bibliotheque et Archives nationales du Quebec between 2002 and 2006. But if the analysis and the inspiration are common, the implementation and the models chosen are distinct but equally interesting: while BAC chose integration, BAnQ preferred harmonization.ction
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".