Transcending taboos and transgressions or merely ploughing towards?
Bibliographic record
Abstract
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.
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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.176 |
| Scholarly communication | 0.030 | 0.058 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.014 |
| 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".