A will-o’-the wisp? On the utility of voluntary contributions of data and knowledge from the fishing industry to marine science
Bibliographic record
Abstract
For future sustainable management of fisheries, we anticipate deeper and more diverse information will be needed. Future needs include not only biological data, but also information that can only come from fishers, such as real-time ‘early warning’ indicators of changes at sea, socio-economic data and fishing strategies. The fishing industry, in our experience, shows clear willingness to voluntarily contribute data and experiential knowledge, but there is little evidence that current institutional frameworks for science and management are receptive and equipped to accommodate such contributions. Current approaches to producing knowledge in support of fisheries management need critical re-evaluation, including the contributions that industry can make. Using examples from well-developed advisory systems in Europe, United States, Canada, Australia and New Zealand, we investigate evidence for three interrelated issues inhibiting systematic integration of voluntary industry contributions to science: (1) concerns about data quality; (2) beliefs about limitations in useability of unique fishers’ knowledge; and (3) perceptions about the impact of industry contributions on the integrity of science. We show that whilst these issues are real, they can be addressed. Entrenching effective science-industry research collaboration (SIRC) calls for action in three specific areas; (i) a move towards alternative modes of knowledge production; (ii) establishing appropriate quality assurance frameworks; and (iii) transitioning to facilitating governance structures. Attention must also be paid to the science-policy-stakeholder interface. Better definition of industry’s role in contributing to science will improve credibility and legitimacy of the scientific process, and of resulting management.
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 | 0.117 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.024 | 0.065 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 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".