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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".