Archives in Ontario: a report on study visits under Canadian Studies Fellowship
Bibliographic record
Abstract
Purpose The Foreign Affairs and International Trade Canada (DFAIT), Government of Canada, through Shastri Indo‐Canadian Institute, encourages Indian scholars for Understanding Canada Faculty Research, Fellowships; the author visited Canada during March 2010 to study Canadian archival system, especially records management in archives in Ontario province; the driving rationale for the study was the realization that there existed very few or no archives and record management (ARM) programs in India. This study intends to report different types of ARM programs in Canada, with special reference to e‐records management, namely automation status, creation of virtual exhibits, preservation of digital images, metadata standard for e‐records, etc. Design/methodology/approach The author visited national, provincial and municipal archives and conducted unstructured interviews with archives staff/managers. Findings Records are considered as commodity and attempts to improve their management has necessitated a more integrated and controlled approach. In this study, it is noted that archivist and records managers have at present very little influence on policy implementation of archives and working under university librarian and less influence on decision making. All the university archives get a nominal amount from library budget. No separate budget for archives is available; modest budgets are allocated for rare book collection. The total archives concept in English Canada is based on the American system. The responsibility for collecting and copying its historical records fell to the government; with the so‐called convergence of technologies helping to fulfill new demands and rising expectations and to empower the end‐user. It was possible to establish very good relations with the archivists of these institutions and the author is keen to keep contact with them. It is expected that this study will serve as a building block to deeper examinations of broader issues such as the core competencies with respect to records management with special reference to e‐records management. Research limitations/implications The prime objectives were to overview the current state of active and passive record keeping in the Government of Canada and identify the core competencies required to build effective record keeping infrastructures in the digital environment that has emerged in most government institutions. During a period of one month, the author was able to visit only selected archives in Toronto and Ottawa. The archives in other parts of Canada are not included in this study due to time limit. Originality/value The paper presents a study of the archival system in Canada and its role and contribution towards promoting a knowledge society in Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.018 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".