Conceiving the records continuum in Canada and the United States
Bibliographic record
Abstract
This thesis surveys the efforts made by Canadian and American records administrators, both records managers and archivists, to ensure that records are created, received, stored, used, preserved, and disposed of in a manner which is both efficient and effective. Beginning with the French Revolution and continuing to modern times, it investigates how approaches in North American archival thinking, government records programs, and applicable records legislation were often flawed because of fundamental misconceptions of the nature of the records themselves. The thesis traces how the most widely accepted approach for administering records, which called for the division of responsibilities amongst records professionals according to the records' "life status" — active, semi-active, or inactive — was incorrect because it was not compatible with the reality that records exist as a conceptual whole and are best administered in a manner which reflects this realization. The records, which should have been managed as a coherent and complete fonds of an institution, suffered from these divisions which had eventually led to the evolution of separate records occupations: those who looked after active records, called records managers, and those who handled inactive ones, labelled archivists. What was required was an "integrated" or "unified" approach such as that articulated by the Canadian archivist Jay Atherton. Like others, he called for the management of records in a manner which reflected the singular nature of the records, an approach which did not make arbitrary divisions where none existed, but instead viewed records from a wider and more complete perspective. Support for this approach amongst some records administrators was precipitated by a number of factors, not the least of which were the demands of handling information in modern society. The thesis concludes by examining what is required for the integrated ideas to be implemented as part of a practical model in today's institutions. It suggests that for the best results to be achieved, records administrators will have to learn to work with others in related information professions, or risk losing the ability to make valid contributions in the modern information age.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".