Evaluating taboo trade-offs in ecosystems services and human well-being
Bibliographic record
Abstract
Managing ecosystems for multiple ecosystem services and balancing the well-being of diverse stakeholders involves different kinds of trade-offs. Often trade-offs involve noneconomic and difficult-to-evaluate values, such as cultural identity, employment, the well-being of poor people, or particular species or ecosystem structures. Although trade-offs need to be considered for successful environmental management, they are often overlooked in favor of win-wins. Management and policy decisions demand approaches that can explicitly acknowledge and evaluate diverse trade-offs. We identified a diversity of apparent trade-offs in a small-scale tropical fishery when ecological simulations were integrated with participatory assessments of social-ecological system structure and stakeholders' well-being. Despite an apparent win-win between conservation and profitability at the aggregate scale, food production, employment, and well-being of marginalized stakeholders were differentially influenced by management decisions leading to trade-offs. Some of these trade-offs were suggested to be "taboo" trade-offs between morally incommensurable values, such as between profits and the well-being of marginalized women. These were not previously recognized as management issues. Stakeholders explored and deliberated over trade-offs supported by an interactive "toy model" representing key system trade-offs, alongside qualitative narrative scenarios of the future. The concept of taboo trade-offs suggests that psychological bias and social sensitivity may exclude key issues from decision making, which can result in policies that are difficult to implement. Our participatory modeling and scenarios approach has the potential to increase awareness of such trade-offs, promote discussion of what is acceptable, and potentially identify and reduce obstacles to management compliance.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".