Chapitre 3. Horizontal Exchange, Relations, and Resistance in Bioart and Practice-based Research
Bibliographic record
Abstract
Bioart sits at the intersection of two relatively elite fields of knowledge specialization and production: Biotechnology and Art. These specializations occupy different strata of the academic hierarchy, requiring credentials and disciplinary rigour that, historically, have tended to validate delineated specificities instead of similarities in research; in turn, these areas of expertise privilege credentialed mastery over other ways of knowing. With its overlap of the arts and the sciences, how might bioart function to flatten existing hierarchies and foster more horizontally collaborative methods towards a shared and critical understanding of bioethics? This paper builds on the notion of horizontal collaboration theorized by Couture et al. (2017), critically attending to the ruptures and resistances (real, perceived, and constructed) that occur when working transversally within verticalized institutions. Combining theoretical interventions with practice-based case studies that deconstruct spaces of bio-artistic inquiry - from the lab or studio to kitchens, classrooms, and galleries - this paper aims to build 'good' relations according to Joanna Zylinska's definition of a body compounding it's relation to another, thereby increasing the power of both.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".