Messy entanglements: research assemblages in heart transplantation discourses and practices
Bibliographic record
Abstract
The paper engages with a variety of data around a supposedly single biomedical event, that of heart transplantation. In conventional discourse, organ transplantation constitutes an unproblematised form of spare part surgery in which failing biological components are replaced by more efficient and enduring ones, but once that simple picture is complicated by employing a radically interdisciplinary approach, any biomedical certainty is profoundly disrupted. Our aim, as a cross-sectorial partnership, has been to explore the complexities of heart transplantation by explicitly entangling research from the arts, biosciences and humanities without privileging any one discourse. It has been no easy enterprise yet it has been highly productive of new insights. We draw on our own ongoing funded research with both heart donor families and recipients to explore our different perceptions of what constitutes data and to demonstrate how the dynamic entangling of multiple data produces a constitutive assemblage of elements in which no one can claim priority. Our claim is that the use of such research assemblages and the collaborations that we bring to our project breaks through disciplinary silos to enable a fuller comprehension of the significance and experience of heart transplantation in both theory and practice.
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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 | 0.125 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.050 | 0.164 |
| Scholarly communication | 0.040 | 0.063 |
| Open science | 0.004 | 0.043 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".