Numbers vs. Narratives: the importance of integrating social science perspectives in ocean sustainability research
Bibliographic record
Abstract
Many disciplines study the ocean and its uses from different perspectives. Recently, there has been a growing awareness about the inseparability of the social and ecological systems and that achieving sustainable use of ocean resources will require the integration of different types of knowledge and disciplines. In this presentation, we will draw from the experience of two early career interdisciplinary scientists to present examples of the role social sciences can play in achieving sustainable oceans management, how and why it should be integrated with other ocean disciplines. More specifically, we will present how a qualitative research approaches to understanding seafood sustainability governance and community/rights-based management makes an important contribution to sustainable ocean management. We conclude that to achieve ocean sustainability, which is a societal problem, we not only need numbers but also the social sciences and their narratives.
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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 | 0.054 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.085 |
| Scholarly communication | 0.025 | 0.053 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".