“Back to the future”: electronic records management in the twenty‐first century
Bibliographic record
Abstract
Canada has tasked itself with delivering e‐government to its citizens by the year 2005 and the Canadian Government has recognized that improving the management of its information holdings is critical to successfully meeting the challenge. As Canadians become accustomed to online services from the private sector, they expect client‐centric and customized service from government and for government to use business processes that make sense when used in an electronic service delivery environment. Technology’s ability to disseminate information quickly and in large volumes, bring an increased need for transparency to e‐government. E‐government increases the need for visible accountability. This in turn, increases the need for accountability for information produced and used by government. It pushes information management from an invisible back office activity into the front lines of service delivery. This article looks at the evolving accountability for managing information within the public service, and some of the approaches the Canadian Government is taking to help address those accountabilities.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".