Thinking with “cow-forming” (De la Cadena)
Bibliographic record
Abstract
Thinking with "cow-forming" (De la Cadena) Roxane Gabet, ULiège How can we think current practices and modes of re-mediation between cows and the "environment"? To do so, I propose to unfold the concept of "cow-forming" (de la Cadena, 2020) in three interrelated ways. First, cow-forming as the trans-formation of actual bodies of cows on a genomic level in order to create a "climate friendly cow". Whether it is as victims of climate change or as causes of it, their roles translate into different interventions - such as increasing their resistance to heat, or reducing their methane emissions. Through a lab ethnography, I will examine how cows' bodies are (re)formed as they become sites of intervention for environmental engineering projects. On a second level, the forming of new cows is at the heart of the current cattle farming industry, relying on artificial insemination. While paying particular attention to reproductive technologies - as they allow for the dissociation of sex and reproduction - I want to inquire into what cow sex is or could be, within and beyond these breeding practices, through an eco-feminist perspective that centres multispecies reproductive justice. Finally, I will focus on the role of cows in forming the land. Based on ethnographic and archival research, I will propose a genealogy that follows how cows were enrolled in colonial projects in Alberta, Canada. Through the massive and forced displacement of people, animals, plants, bacteria and viruses, settler colonialism reshaped entire ecosystems in a process of terra-forming, of which cows are part of: that is, a process of cow-forming.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".