Doing More-Than-Human Research: Developing Qualitative Research Methods for a Multispecies World
Bibliographic record
Abstract
Increasingly, qualitative researchers are considering the more-than-humans that Euro-Western social science largely has ignored. But if social science research methods were made for humans, how does one include everything else? In this article, five scholars describe the methodological steps they each have developed. We explore the role of onto-epistemology as an analytical tool to understand relationships between people and food-producing plants in Toronto migrant gardens; the technique of multimodal noticing used to account for animal agency in fieldwork with women who raise small livestock in Egypt; the use of time as a methodological tool to study human-plant relations in German commercial orchards; a methodological praxis that works with posthuman disability studies to become-with a disability event-assemblage; and how to operationalize the notion of ontological multiplicity in ethnographic fieldwork in a case study of a river in Mexico. These ways of conducting research may lead to innovative responses to complex global challenges.
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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 | 0.376 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".