Towards vibrant fish populations and sustainable fisheries that benefit all: learning from the last 30 years to inform the next 30 years
Bibliographic record
Abstract
A common goal among fisheries science professionals, stakeholders, and rights holders is to ensure the persistence and resilience of vibrant fish populations and sustainable, equitable fisheries in diverse aquatic ecosystems, from small headwater streams to offshore pelagic waters. Achieving this goal requires a complex intersection of science and management, and a recognition of the interconnections among people, place, and fish that govern these tightly coupled socioecological and sociotechnical systems. The World Fisheries Congress (WFC) convenes every four years and provides a unique global forum to debate and discuss threats, issues, and opportunities facing fish populations and fisheries. The 2021 WFC meeting, hosted remotely in Adelaide, Australia, marked the 30th year since the first meeting was held in Athens, Greece, and provided an opportunity to reflect on progress made in the past 30 years and provide guidance for the future. We assembled a diverse team of individuals involved with the Adelaide WFC and reflected on the major challenges that faced fish and fisheries over the past 30 years, discussed progress toward overcoming those challenges, and then used themes that emerged during the Congress to identify issues and opportunities to improve sustainability in the world's fisheries for the next 30 years. Key future needs and opportunities identified include: rethinking fisheries management systems and modelling approaches, modernizing and integrating assessment and information systems, being responsive and flexible in addressing persistent and emerging threats to fish and fisheries, mainstreaming the human dimension of fisheries, rethinking governance, policy and compliance, and achieving equity and inclusion in fisheries. We also identified a number of cross-cutting themes including better understanding the role of fish as nutrition in a hungry world, adapting to climate change, embracing transdisciplinarity, respecting Indigenous knowledge systems, thinking ahead with foresight science, and working together across scales. By reflecting on the past and thinking about the future, we aim to provide guidance for achieving our mutual goal of sustaining vibrant fish populations and sustainable fisheries that benefit all. We hope that this prospective thinking can serve as a guide to (i) assess progress towards achieving this lofty goal and (ii) refine our path with input from new and emerging voices and approaches in fisheries science, management, and stewardship.
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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.014 |
| 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".