Erotic. Maternal. Cultural. Symbolic. Medical. What are breasts? How are they imagined? And who gets to decide?
Bibliographic record
Abstract
he three of us, each in our own way, have varied and long term relationships with breasts.Growing up, we have experienced years of watching our own bodies change before our very eyes, often mediated through the male gaze.And that changing terrain that we call our body-self continues to shift and alter, as reproduction, aging, and-for one of us-breast cancer leave their marks.Through their presence or absence, breasts are largely visual, even more so through their objectification and fragmentation in media representations and advertisements as well as medical imagery and cosmetic procedures, reconstructions, and prostheses.These visual "imagings" of our ever-changing breasts, along with their inherent fluidity and textures, prompted us to begin ongoing conversations that are occurring across a number of disciplines, including humanities, social sciences, and the arts, but also biology, oncology and medicine.Breast imaging in medicine has shaped how we understand these material objects as self-evident.At the microscopic level, the medical gaze concentrates on breast tissue as a form of synecdoche.Such medical imaging informs surgeons as to the location of breast tumour tissues to be removed, and radiologists as to where to direct radiotherapy, either to debulk tumours prior to surgery, or to eradicate potential remaining malignant cells after removal of the primary tumour.These procedures suggest that breasts are contingent
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.017 |
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".