CFRN Canadian Fisheries Research Network
Bibliographic record
Abstract
Een samenvatting: The need for social science in fisheries management and research Keynote by Dr. Marloes Kraan, IMARES Wageningen University, the Netherlands The keynote was built up around two questions: 1. ‘why is (or should) social science be a crucial part of fisheries management and research?’ and 2. ‘how can it be more integrated with other disciplines?’ It was argued that the truism ‘fisheries management is about managing people’ in fact asks for social science [anthropology, sociology, human geography, ...] to be part of the research package. Although influencing human behaviour is the key focus of management action, the core of the science is done by biologists and economists. This has impacted negatively on the understanding of human behaviour (of fishermen in this case) in fisheries science and management. The keynote provided the research areas of interest of social scientists and explained some of the key aspects of social science research. It touched upon the fact that social science still plays a relatively marginal role, but pointed out that things seem to be changing. Kraan shared her own experience of working as a social scientist from within a biological / ecological research institute and argued how that made it easier for her to contribute to applied research as a social scientist. By doing so she works on integrating social science methods and approaches in natural science or transdisciplinary research projects. There are a number of advantages to work together as a social scientist with other disciplines in the marine field, and the cooperation can take different forms; offering social science methods for natural scientists, interdisciplinary research but also social science research alongside the work of the other disciplines on certain topics. As an example of the latter she presented a part of the GAP2 case study of the Netherlands on discards, within which she was able to study, together with Dr. Marieke Verweij (Pro Sea), the perceptions of fishermen and policy makers about discards. This work has been instrumental research in the national context showing the gap between industry and policy, which potentially undermines current practices of cooperation in the implementation of the landing obligation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.303 | 0.097 |
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".