Exploring the potential impacts of machine learning on trust in fishery management
Bibliographic record
Abstract
Abstract Recent literature and empirical research show that both trust and collaboration are of great importance for effective fishery management. The application of Machine Learning (ML) to fishery management offers exciting new opportunities for data synthesis and analysis and integrated insights across typically siloed domains. Yet, challenges remain as ML approaches provide new means of monitoring, enforcement and data analysis. Trust is among the underlying bases of collaboration, and control is the main means of shaping collaborative decision‐making techniques. As ML changes the dynamics of governance and enhances management control mechanisms, ML affects trust. ML methods are being introduced into a context that suffers a lack of transparency and trust between fishers and managers. As ML technologies continue to be used to inform fishery management and influence knowledge sharing and communication within the fishery network, forms of trust existing in the management network will be impacted differently. This article provides a concise review of a subset of potential ML applications to fishery management to explore how these emerging methods may impact forms of trust between fishery stakeholders.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".