Researching Contested Companionship
Bibliographic record
Abstract
June 8th, 2016 ended the lives of both Christiane Vadnais and Lucifer, but it also unraveled many other relations between humans and pitbull-type dogs. In this paper, I explore what it meant to conduct multispecies ethnography in the context of Breed Specific Legislation (BSL) in the city of Montréal between 2016-2018. I detail how methodologies of participant observation, walking interviews, and auto-ethnography explored themes of care, ethics, solidarity, and intervention. In the first section, I describe who I am engaging with when I say ‘pitbull-type dog’. It is here I define what I call contested companionship. Next, I turn to my fieldwork detailing three research methods. I first describe participant observation and rapport building at Tails and Paws Montréal, a dog care facility, and introduce Rocky, a pitbull-type dog that had to evacuate the province of Québec. Next, I outline the benefit of walking interviews for multispecies research. I conclude my reflection on methodological practices by drawing from auto-ethnographic data outlining my shared life with Clementine and Eleanor. In the third section, I consider questions of intervention in the field that were deeply tethered to matters of life and death for both human and nonhuman participants, in particular for Fred and his companions Marilyn and Samson. Scaffolded together, I provide an example of what an ethically informed multispecies research design looked like that additionally had to navigate contested companionship –illegal or precarious—that shaped methodological practices into politically productive strategies that safeguarded not only individuals but relationships.
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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 | 0.006 | 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 | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".