Environmental and Natural Resource Economics and Systemic Racism
Bibliographic record
Abstract
This article highlights some ways in which scholarly work in environmental and natural resource economics may be affected by, and may unintentionally further, racial inequity. We discuss four channels through which these effects may occur. The first is prioritization of efficiency over distribution. The second is inattention to procedural justice. The third involves abstraction away from crucial historical or social contexts. The fourth is a narrow focus on problems that fit neatly within existing analytical and empirical frameworks. We follow these threads through three areas in which we offer examples of how environmental and natural resource economics work may further racial inequity. The first involves methods of evaluating and measuring human and social welfare. The second relates to policy modeling choices. The third centers on analysis of management of the commons. We document opportunities to improve the field by better considering how racial inequity may affect, and be affected by, environmental and natural resource economic analysis. Scholars in this field have tools that can mitigate systemic racism in access to natural resources and a clean environment, but work must be done before that potential is realized.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".