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Multiplicity and ontological instability in nonhuman hearts

2022· article· en· W4285198842 on OpenAlexaff
Marisol Marini, Marko Monteiro, Jenny Slatman

Bibliographic record

VenueSaúde e Sociedade · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsMcGill University
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsMateriality (auditing)SemioticsScope (computer science)EpistemologyEthnographyEngineering ethicsSociologyCognitive scienceKnowledge managementComputer scienceEngineeringPsychologyAestheticsAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract This paper reflects on the relationship between biological bodies and technological artifacts, based on ethnographic research on the development of circulatory assist technologies, known as artificial hearts. To understand the embodiment that such mechanical devices help to produce, we aim to characterize two types of bodies enacted from medical practices and biotechnologies designed for patients with advanced heart failure. The immunological bodies, produced from heart transplantation, will be contrasted with the bionic bodies, composed of the assembly with artificial hearts. We propose that it is necessary to consider each of these technologies as co-produced with different natures, supported by specific materialities, practices, moralities and assumptions. The attention given to practices and materiality will allow to highlight the various material-semiotic intertwinings. Tracing the development trajectory of this field will allow exploring the imagination from which such interventions emerge and the transformations that have occurred, emphasizing the link to the body-machine woven in the biomedical scope.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0070.061
Scholarly communication0.0100.010
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.096
GPT teacher head0.336
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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