Economics of Prioritising Environmental Research: An Expected Value of Partial Perfect Information (EVPPI) Framework
Bibliographic record
Abstract
Significant public funds are spent on projects designed to improve environmental quality. Design and implementation of these initiatives is contingent on knowledge generated from environmental research. Funding agencies have many demands for research dollars while having limited research budgets. A prioritisation process is required for efficient and effective allocation of research funds. A review of research prioritisation literature suggests that ad hoc approaches are often used for ex ante analyses examining the value of environmental research (e.g., Delphi techniques, information gaps from literature reviews). This paper characterises environmental research prioritisation in the form of an economic decision problem, formulated using expected value of information concepts. An implicitly Bayesian modelling approach is developed with research priorities being made based on estimates of expected value of partial perfect information (EVPPI). Considerations and challenges associated with empirical implementation of EVPPI are discussed and a hypothetical example is provided to illustrate use of this approach in informing environmental research funding decisions.
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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 | 0.039 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".