Growing into Interdisciplinarity: How to Converge Biology, Economics, and Social Science in Fisheries Research?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has been acknowledged that natural sciences alone cannot provide an adequate basis for the management of complex environmental problems. The scientific knowledge base has to be expanded in a more holistic direction by incorporating social and economic issues. As well, the multifaceted knowledge has to be summarized in a form that can support science-based decision making. This is, however, difficult. Interdisciplinary skills, practices, and methodologies are needed that enable the integration of knowledge from conceptually different disciplines. Through a focus on our research process, we analyzed how and what kind of interdisciplinarity between natural scientists, environmental economists, and social scientists grew from the need to better understand the complexity and uncertainty inherent to the Baltic salmon fisheries, and how divergent knowledge was integrated in a form that can support science-based decision making. The empirical findings suggest that interdisciplinarity is an extensive learning process that takes place on three levels: between individuals, between disciplines, and between types of knowledge. Such a learning process is facilitated by agreeing to a methodological epoch and by formulating a global question at the outset of a process.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it