Renegotiating Species Belonging in a Changing World
Bibliographic record
Abstract
Globalizing forces keep creating new “alien” or “introduced” species, but what should be done about these new nonhuman neighbors is contested. Arguments about whether species belong often bundle together claims about what is and what should be, which can create an uneven playing field that overlooks the diversity of human relations with new neighbors. This paper aims to distinguish a constructive path forward by distilling four ways that species are deemed to belong. From the literature, we identify nativeness, wildness, contributions, and right relations as distinct registers of belonging. We elaborate on the values and worldviews that each register enfolds, and who typically benefits from these regimes. Drawing from case examples, we show how this conceptual framework can help to expose the wider meanings and effects of new neighbors, broaden decision-making beyond technocratic debates, and compose new alternatives for conservation and wildlife 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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.053 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".