Policies of Managing Web Resources at the Canadian Government: A Records Management Perspective
Bibliographic record
Abstract
As the importance of Web resources has increased greatly to organizations, this study examines the Web management policies and guidelines of five Canadian government agencies, studies the definitions and characteristics of Web resources from a records management perspective and presents the current policies relevant to the appraisal, retention, and preservation…Comme la quantité des ressources web a fortement augmentée dans les organisations, cette étude examine les politiques de gestion du web et les directives de cinq organismes gouvernementaux canadiens. Elle étudie également les définitions et les caractéristiques des ressources web à partir d’une perspective de gestion des documents et présente les politiques actuelles relatives à l’évaluation, la conservation et la préservation…
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".