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Record W2941682117 · doi:10.1386/tear.16.3.277_1

Transcending taboos and transgressions or merely ploughing towards?

2018· article· en· W2941682117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTechnoetic Arts · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFoucault, Power, and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMirroringPresentation (obstetrics)Variety (cybernetics)Point (geometry)Subject (documents)SociologyRaw dataEpistemologyNaggingPsychologyAestheticsComputer scienceSocial psychologyArtificial intelligenceCommunicationArtPhilosophyMedicine

Abstract

fetched live from OpenAlex

Among the most enduring taboos, those related to the human body are the most enduring, throughout history. Be it its re/presentation of exploration, it constituted for most cultures and epochs a very sensitive subject, ever evolving and changing, but perennially raw and open to debate and discussion. With the advent of new technologies sustaining and infiltrating society, the body is seen, explored and represented in new ways that can be, simultaneously, interpreted either as transgressive or respectful of taboos, depending on the point of view or the current social norms. The question is: are we able to transcend transgression of taboos through our work or are we still far from achieving it? Our tools are computer-generated simulations based on patient-collected data. Building our arguments on Foucault’s Birth of the Clinic (1973) analysis, we approach the evolution of our own research as a ‘case study’. In the age of the life-support, stem-cell therapy and 3D organ printing, is computer simulation mimicking or mirroring organic life and phenomena, or is it creating a debatable simulacrum? From reducing the human body to a series of equations that lead to depersonalization and loss of self, to tailored modelling based on patient-collected biological data, the computer simulations took a variety of guises. At each point of the way, confronting the taboos, conventions and through controlled transgressions of established rules, we strived to transcend all historic limitations and update, adjust and fine-tune the technology and its uses to better suit the clinicians’ pursuits.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.418
Teacher spread0.303 · 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