Bibliographic record
Abstract
This paper explores the idea of ambivalent speciesism—speciesism that expresses itself both in hostile and benevolent attitudes and behaviours, while remaining, overall, disrespectful or inconsiderate towards members of certain species. It has long been acknowledged that phenomena such as racism and sexism are marked by ambivalence. The same is likely to be the case with respect to speciesism. This prospect has conceptual implications because making sense of positively valenced components of speciesism requires clarifying the connection between discrimination and prejudice. After raising this conceptual issue, this paper focuses on outlining possible patterns of ambivalent speciesism, distinguishing ambivalence from complex negativity and indicating benevolent speciesism’s potential to harm animals. Benevolent speciesism can come with some local benefits for some animals but eventually harms them by working as a facilitating factor for their subordination, underpinning negligence and entailing punishment when positive stereotypes are disappointed. While hostile speciesism rightly draws our attention, we should also look out for its positive forms which are likely to become more practically relevant as efforts for the recognition of animals’ moral considerability are underway.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".