Thinking-together through ethical moments in multispecies fieldwork: dialoguing expertise, visibility, and worlding
Bibliographic record
Abstract
The recent proliferation of multispecies research contains a conspicuous gap when it comes to the methodological and ethical dimensions of navigating relations with more-than-human participants. Although codified protocols can be a useful starting point, the ethical tensions that inevitably emerge during fieldwork are often fetishized in final outputs. Whilst calls to ‘stay with the trouble’ are important, they often remain descriptive and un-actionable. In contrast, this paper offers a method for working through these tensions, asking what obligations they place on researchers and how they might be negotiated in practice, without slipping into advancing prescriptive rules or guidelines. We discuss this in the context of a range of ‘ethically important moments’ that we each encountered in the field, which were both complex and ambiguous. During our respective periods of fieldwork with dogs in Chornobyl and urban coyotes in Canada, we have each faced moments in which rapid decisions must be made as we navigate the affective intensities that move us as geographers, participant observers, and community members. In this paper, we perform and reflect upon Kohl and McCutcheon’s (2015) ‘kitchen table reflexivity’ as one approach for working through these moments, not just staying with them. Here, ethical tensions are worked through via dialogue. This paper is both method and product, as stories from our individual research are brought into dialogue around three fraught dimensions of multispecies research: <i>negotiating expertise and positionality</i>, <i>making visible or concealing the animal</i>, and <i>intervening in animal worlds</i>.
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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 | 0.056 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.036 | 0.112 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".