The Perverse Incentives of Climate Integration: Why Researchers Can't Deliver What Funding Institutions Demand
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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