Review of: "Biological perspectives on complexities of fisheries co-management: A case study of Newfoundland and Labrador snow crab"
Bibliographic record
Abstract
Mullowney et al. 2020 [1] state there is little-to-no evidence that co-management has positive benefits for species abundance and sustainability.Using a case study of the Newfoundland and Labrador (NL) snow crab fishery as their context, the authors argue that the NL snow crab management system is promoting problematic processes that prevent the best possible scientific advice.This perspective is problematic because Mullowney et al: 1) erase the presence of an already-functioning Indigenous co-management system in NL and Inuit rights related to fisheries management by omitting mention of it in the article; and 2) rely on a predominately biological sciences perspective, missing key opportunities from Indigenous and social sciences [2] to inform not only science on the snow crab fishery, but on fisheries sciences generally.First, Mullowney et al. failed to accurately and adequately define the snow crab co-management system in Newfoundland and Labrador and, in particular, did not acknowledge or mention the presence of the Torngat Joint Fisheries Board, emergent from the Labrador Inuit Land Claim Agreement.The Torngat Joint Fisheries Board co-management structure is constitutionally protected and has been in place since 2005 [3] , bringing together representatives from the Nunatsiavut Government and the provincial and federal governments.Inuit from Nunatsiavut spent over 50 years negotiating their land claim and self-government agreement, including the provisions for the co-management of snow crab [4] .The snow crab (Chionoecetes opilio), known as Putjoti in Inuktitut [5] , is an important livelihood resource in the Labrador Inuit Settlement Area.Labrador Inuit harvest snow crab in NAFO areas 2HJ and these crabs are processed within the Inuit community of Makkovik.Over the most recent five-year period between 2015-2020, the average landed value to the Qeios, CC-BY 4.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".