Integrating equity and justice in marine ecosystem models: An incremental but meaningful approach
Bibliographic record
Abstract
• We invite modellers to dare to ask different questions & bring knowledge together. • Ecosystem models should better represent the realities we see & the impact on fishers. • Equity focus leads to better advice for decision makers & more meaningful solutions. • We show 3 ways to integrate equity in ecosystem models with different difficulties. The notion of equity is a complex and multifaceted one, and it can be difficult to operationalize in a meaningful way. Nevertheless, the importance of integrating equity and justice concerns in environmental management is quickly being recognized across disciplines, including ocean sciences that have long engaged with complex dynamic systems. Ecosystem modelling approaches can be particularly helpful given their ability to incorporate a wide range of concepts, information, and management goals. However, including social equity in ecosystem models is perceived as a difficult task and most marine ecosystem models still mainly focus on fish stock and ecological dynamics and outcomes, ignoring social impacts, which risks losing opportunities to help improve the lives of fisherfolk and identify meaningful solutions. Here, we propose ways to integrate equity in ecosystem models at three different levels. From more to less demanding, we can: (1) explicitly model equity, (2) slightly adjust existing models to incorporate key human components, and (3) ask new questions with existing models. As we move along these steppingstones, we must listen and learn from community partners and social scientists on what data are needed, how to handle ‘unconventional’ data types, and what indicators are most useful. To do so, we invite other modellers to start thinking differently, dare to ask different questions, and bring knowledge together so that our ecosystem models better represent the realities we see.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.024 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".