Balancing privacy with access to information for commercial fisheries data: A critical review of Fisheries and Oceans Canada’s “rule of five” policy
Bibliographic record
Abstract
Although Canada’s oceans are a public resource, commercial fisheries data are routinely withheld from researchers and the general public by Fisheries and Oceans Canada (DFO) due to privacy obligations. However, data can be released if considered sufficiently de-personalized through an internal guideline called the “rule of five,” under which data sources are aggregated to a threshold of five to allow for data publication or disclosure. This article provides an overview of the “rule of five,” summarizes key legislative provisions that have bearing on the “rule” and potential for its reform, and discusses the findings from two tools used to collect information on the “rule” and its use in Canada: (1) an Access to Information and Privacy request and (2) an anonymous survey conducted to evaluate the impacts of the “rule” on various stakeholders. The “rule of five” is not mandatory but rather represents a conservative approach to access to information that can be detrimental to independent researchers and the public interest in transparent fisheries data. The article concludes with recommendations to further a rebalancing of privacy and access to information, including emphasizing existing legislative exemptions that could allow for data disclosure when the “rule of five” is not met.
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 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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".