Learning, thinking, sharing, and working across boundaries in fisheries science
Bibliographic record
Abstract
Abstract Fisheries science and practice are challenging and require learning, thinking, and sharing across boundaries. The idea of boundary crossing can be described as some form of multiple disciplinarity (e.g. interdisciplinarity, transdisciplinarity), yet that inherently implies that the boundaries crossed are purely disciplinary in nature. After working across various other boundaries (i.e. realms, regions, disciplines, sectors, domains, and knowledge systems) for most of our educational journeys and professional careers, we reflect on our lived experiences with a focus on identifying the benefits and challenges of engaging in different types of boundary crossing. We submit that our personal and professional lives have been enriched by stepping outside of our immediate comfort zones and expertise (i.e. fish ecology) and engaging in active listening and learning with colleagues in other disciplines (i.e. various social sciences) and with stakeholders and policymakers. We have learned much from working across boundaries and encourage others, especially early career professionals, to do the same. What may superficially appear to be a bridge too far may in fact provide novel ways of thinking about a given issue or topic that generates actionable science for sustainable fisheries management and conservation. Many of the projects that we consider to be our greatest successes represent ones that involved boundary crossing, examples of which we provide in this essay. There is a need to prepare the next generation of problem solvers for engaging in boundary crossing and celebrating examples of where such efforts have led to meaningful advances in fisheries science and practice. Ensuring that institutional and cultural barriers that may constrain boundary crossing are addressed while also supporting those doing such work will be key to address the many fisheries and aquatic science challenges of today and tomorrow in both marine and freshwater systems.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.055 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".