What can information systems do for regulators? A review of the state-of-practice in Canada
Bibliographic record
Abstract
Regulations constitute a rich source of requirements for software systems, especially so for information systems that handle sensitive data. However, there has been little attention paid to regulators and their requirements for managing the regulatory lifecycle. This paper presents a study of the state-of-practice for regulators in Canada by examining seven Government of Canada (GoC) agencies responsible for regulations. In each case, we attempt to capture the context within which regulations are created, the motivation behind these regulations, and the practices related to their design, enforcement, and review. Our aims are to understand how regulators currently design, monitor, and assess regulations and other regulatory instruments in their respective domains, and to identify opportunities where information system (IS) solutions can be applied to improve practice. Our field study involved reviewing publicly available information and conducting informal interviews. Together, these activities helped us understand key regulators' activities and concerns, as well as important challenges they currently face. In this paper, we summarize our findings and explain the implications for the use of ISs to improve the practice of regulatory management in the form of a research agenda.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".