The Perverse Incentives of Climate Integration: Why Researchers Can't Deliver What Funding Institutions Demand
Bibliographic record
Abstract
ABSTRACT Research funders increasingly require integration of future climate projections across health, agriculture, fisheries, and development economics, creating perverse incentives: institutions demand what current climate science cannot reliably deliver. I use “perverse incentive” here in its standard economic sense: an incentive that unintentionally produces counterproductive behavior, rather than implying ill will on the part of funders. Climate models designed for global, long‐term analysis are being misapplied for short‐term, regional uses beyond their validated scope. This paper identifies three problems arising from this mismatch: maladaptation in scientific labor allocation, erosion of trustworthiness through representational overextension, and representational risk from harmful signaling and normalization of inappropriate methodological norms. Researchers include climate projections not because they are justified, but because they are required, transforming models from tools of inquiry into performances of compliance. This threatens both scientific integrity and the legitimacy of science underwritten by democratic norms. Three institutional reforms are proposed to realign incentives with epistemic responsibility and ensure climate science serves as a reliable policy foundation rather than mere signaling.
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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 | 0.227 | 0.385 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".