Bibliographic record
Abstract
Two protracted debates about the moral status of animals in ecological restoration projects are discussed that both testify to the troubling aspects of our inclination to think in terms of dualisms and dichotomies. These cases are more or less complementary: the first one is about the (re)introduction of species that were once pushed out of their native environment; the other one concerns the elimination or eradication of “exotic” and “alien” species that have invaded and degraded ecosystems. Both cases show the detrimental impact of dualistic thinking on ecological restoration projects. In the first case, communication and cooperation between stakeholders is frustrated by the opposition of zoocentrism and ecocentrism; in the second case the opposition of nativism and cosmopolitanism appears to be a major stumbling block for consensus building and conflict management. I will argue that “gradualization”—thinking in terms of degrees instead of boundaries—can offer a way out of this black-and-white thinking and can open up space for negotiation and deliberation among different and sometimes diverging perspectives.
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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.116 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".