Politics driving efforts to reduce biodiversity conservation in the United States
Bibliographic record
Abstract
Despite global calls to raise protection for nature, efforts proliferate to reduce the extent of, and restrictions in, protected areas (PAs) via legal changes to downgrade, downsize, or degazette PAs (PADDD). Protected area downgrading, downsizing, and degazettement studies have considered the tropics, despite significant data and relevance for the Global North, and focused on fixed proxies for economic opportunity cost. Given important political dynamics, we focus instead on the U.S. and shifts in political representation. We examine 2001–2018 federal PADDD events in the U.S., using panel data to control for all fixed factors. We study how elections that shift representatives and senators affect U.S. PADDD. Indeed, shifts at district, state, and national levels appear to influence PADDD. Specifically, shifts that put Republicans into office raised risks for PADDD events, especially proposals. Our empirical results highlight shifts in political power as an ongoing challenge to conservation, even after the establishment of protected areas.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".