Ratchet effects revisited: power effects and systematic bias in natural resource management
Bibliographic record
Abstract
Regulatory ratchets arise when governance appears to be effective, but actually masks a steady loss of natural capital. This occurs when biases in environmental impact assessment (EIA) systematically underestimate the true impact of large developments, generated by statistical convention fixing α at 0.05 (Type 1 error or false positive rate; i.e., the probability of concluding that a development will have an impact when there is none) while β, the false negative rate (failing to detect a true impact, or Type 2 error), is often fixed at 0.2. This asymmetry (β > α) generates a higher likelihood of mistakenly permitting development than mistakenly preventing it. Beyond statistical bias in EIA, routine environmental regulations are often ineffective due to low compliance, inadequate thresholds, and broad exemptions, which tend to cryptically institutionalize net loss. Measuring bias and inefficiency of environmental regulation is foundational to correcting regulatory ratchets and identifying pathways towards no net loss. Like net loss from major developments, cumulative net loss from inadequate routine environmental protections also needs to be estimated and offset by active habitat restoration; this should be delivered as a core program of resource management agencies, with the goal of fully integrating the mitigation hierarchy into routine natural resource governance.
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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 | 0.073 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".