The Rhetoric of Delisting Species Under the Endangered Species Act: How to Declare Victory Without Winning the War
Bibliographic record
Abstract
The recovery and delisting of species protected under the Endangered Species Act is the coming fashion and no mistake. This spring many of us followed with interest the nesting trevails of California condors in California and Arizona as the birds endeavored to lay the foundations for a comeback. At the same time, we watched with mixed feelings building pressure to delist gray wolves and the announced delisting of the Aleutian Canada geese. The United States Fish and Wildlife Service has committed itself to recovery as the goal for its species protection program. Unfortunately, under the provisions of the law and the logic of politics there is great pressure to measure the success of recovery efforts in terms of species delisting. Recovery may have the power to transform the popular image of the Endangered Species Act from a statute about stopping development into a statute about preserving species. However, only delisting can, in theory, decouple protection of biodiversity from the much maligned business of getting government permits and dealings with federal officials. Like it or not, the common notions of recovery and delisting - bringing species to the point at which they are so numerous and so well distributed in sufficient quantities of perpetually secure habitat that the protections provided by the Endangered Species Act become unnecessary - will not become a realistic aspiration for any significant number of species any time in the foreseeable future. Yet there is political pressure to show results by declaring species recovered and removing them from the lists of protected species. Can the federal government emphasize species recovery and delisting in the face of collapsing global ecosystems? The answer, of course, is yes. The real question is how.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.059 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.019 | 0.041 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".