The DFO Experience with Inclusive Advisory Processes
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.In 1996 the Department of Fisheries and Oceans Canada re-instituted a national coordination office for ensuring peer review and provision of science advice on fisheries issues. When Canada’s Oceans Act and the Species-at-Risk Act were passed in the late 1990s, the mandate for coordination of peer review and provision of advice was extended to cover the science support for these pieces of legislation, and all other substantive peer review and advisory needs of the policy and management sectors of the Department. From the outset the Canadian Science Advisory Secretariat and the regional offices were instructed to ensure that “experiential knowledge” was brought into the body of information used in assessments and advice, which required fishermen to participate actively in the assessment process. When the federal government approved the Principles and Guidelines for Science Advice for Government Effectiveness, the standards for engagement were raised. To ensure inclusiveness of all types of information and knowledge, and transparency of review and advisory processes, the Principles and Guidelines required full participation in the entire process by individuals from groups whose lives would be directly affected by the scientific advice. CSAS and the regional offices have tried a number of different approaches to meeting these standards for inclusiveness and transparency. Some have failed badly, some have usually succeeded, and many have a patchy performance record. Over time we have developed guidance for “best practice” in making our review and advisory processes inclusive and transparent, without sacrificing scientific quality or independent. The presentation will review these “best practices” and lessons learned from the Canadian experience.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".