Bibliographic record
Abstract
Despite the growing richness of multispecies scholarship, questions about anthropomorphism – how to responsibly speak about other species as beings with their own lifeworlds and intentions without anthropomorphizing – continue to haunt multispecies research in Western academic settings. Here I argue that working to attend ethically to more-than-human others as beings with their own lifeworlds and decolonize Western epistemologies as a joint project can help multispecies researchers address the conditions that render charges of anthropomorphism sensible to begin with. I first introduce my study context at the Vancouver Aquarium and positionality as a settler scholar, reflecting on how these come together to generate tensions that shape the meaning of (and possibilities for) ethical multispecies research. I then explain how I have looked to Indigenous intellectuals for guidance before exploring submerged grammars of animacy that linger within the Vancouver Aquarium and Western epistemologies enfolded with this space. I engage Indigenous, feminist, and queer scholarship with more-than-human geographies and octopus science to explain how imagining ethical attention to more-than-human others as beings with their own lifeworlds from this space also entails imagining radically different relations between bodies and spaces than those permitted at the Aquarium.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".