Finding and Using Archival Resources: A Cross-Canada Survey of Historians\t\t\t\tStudying Canadian History
Bibliographic record
Abstract
This paper reports the results of a 2001 postal questionnaire (English and French) that gathered information about historians' use of archival resources. The population for this report consisted of faculty members in history departments in degree-granting institutions in Canada whose area of interest is the history of Canada. The survey probed their current information-seeking practices in archives, invited assessment of their experience doing archival research, and sought their preferences for developments in the future. The conclusions indicate that finding and using sources in the early twenty-first century continues to invoke the knowledge and expertise of archivists.RÉSUMÉLes auteures présentent dans cet article les résultats d'un questionnaire postal de 2001 (en anglais et en français) sur l'usage par les historiens des ressources d'archives. La population ciblée par cette étude était constituée de professeurs dans les départements d'histoire d'universités canadiennes ayant pour domaine de recherche l'histoire du Canada. Le sondage a exploré leurs pratiques en recherche d'information au sein des institutions d'archives, les a invité à évaluer leur expérience de recherche et à faire connaître leurs préférences quant aux développements futurs. Les conclusions indiquent que, pour trouver et utiliser des sources dans ce début de 21e siècle, les connaissances et l'expertise des archivistes sont toujours nécessaires.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.040 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".