Revised estimates of racial and ethnic disparities in rooftop PV deployment in the United States
Bibliographic record
Abstract
We report our discovery of significant problems with Sunter, Castellanos, and Kammen’s (SCK) recent study of racial and ethnic disparities in rooftop PV deployment published in Nature Sustainability. First, we identify irregularities in SCK’s reported procedures and results that are statistically implausible. Second, we report results from a failed replication of their analyses. We correct the implausible absolute deployment figures and identify racial and ethnic disparities that differ substantially from those reported by SCK. We also extend SCK’s analysis, showing that white-majority tracts have the largest deployment advantages in states with the most developed PV markets, whereas deployment estimates in Black and other minority-majority tracts are (artificially) inflated by relatively higher deployment in states with very little solar. In our view, an accurate accounting of racial and ethnic disparities in PV deployment is necessary to develop interventions that effectively and equitably promote sustainable energy transitions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".