Clarifying misconceptions of extinction risk assessment with the IUCN Red List
Bibliographic record
Abstract
The identification of species at risk of extinction is a central goal of conservation. As the use of data compiled for IUCN Red List assessments expands, a number of misconceptions regarding the purpose, application and use of the IUCN Red List categories and criteria have arisen. We outline five such classes of misconception; the most consequential drive proposals for adapted versions of the criteria, rendering assessments among species incomparable. A key challenge for the future will be to recognize the point where understanding has developed so markedly that it is time for the next generation of the Red List criteria. We do not believe we are there yet but, recognizing the need for scrutiny and continued development of Red Listing, conclude by suggesting areas where additional research could be valuable in improving the understanding of extinction risk among species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.193 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.007 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 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".