A tale of two committees : evaluating collaborative management planning in Canada's Pacific groundfish fisheries
Bibliographic record
Abstract
Governing agencies increasingly employ collaborative forms of decision-making in fisheries management to improve decision quality and legitimacy. However, crafting fair and effective collaborative processes which will achieve these benefits is often difficult. In an effort to identify keys and obstacles to success, this research evaluated the Commercial Groundfish Initiative, a collaborative planning process tasked with reforming the management of Canada's Pacific groundfish fisheries. Using semi-structured interviews, I gathered the perspectives of participants from the two committees within the process: a consensus-based committee of commercial representatives and a committee broadly representative of other interest groups for which consensus was encouraged but not mandated. Control over the design of a proposal for management reform was asymmetrically divided between the two committees, giving the commercial committee the primary role. Participants from the commercial committee expressed high levels of support for their consensus process. Keys to this committee’s success in reaching a high quality agreement were (i) a strong incentive to cooperate, (ii) consensus decision-making, and (iii) independent process facilitation. The latter two functioned as security measures against the potential for process manipulation by participants or governing agencies. Results from an examination of the broader committee indicate non-commercial respondents were largely accepting of an “oversight” role provided that the scope for their input remained sufficient, which it did not. Early involvement in tasks such as designing the process and defining objectives were particularly critical to non-commercial respondents’ perceptions of procedural fairness and their ability to participate effectively. Several participants also raised concerns that the process was not appropriately representative of groups with an interest in groundfish management. The poor performance of the process in these respects overshadowed positive aspects of broadening participation beyond commercial users. Consensus approaches have gained currency among commercial participants as a result of their positive experience and made some of them more willing to consider meaningful collaboration with a broader range of interest groups. The ineffectiveness of the broader committee suggests there is still work to do in designing processes that will actually achieve this meaningful, broad collaboration.
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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 | 0.180 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".