Bibliographic record
Abstract
Without the authority of legislation in the United Kingdom, some bequeathed their bodies to physicians, surgeons and apothecaries to dissect and create anatomical specimens in the eighteenth and early-nineteenth centuries. Those individuals included the legal and political philosopher Jeremy Bentham, who was publicly dissected and whose skeleton and preserved head were used to prepare the ‘Auto-Icon’. Other dissections were publicised in newsprints and periodicals, often alongside commentary on the prejudice against dissection and anatomy and calls for anatomy legislation. Such bequests attempted to interface with English law generally and anatomy law specifically, but how they did so and with what effect are less obvious. Accordingly, the author undertakes the study of body bequests before legislation, so to identify and analyse their significance to the law’s conception of what could be done with or to a human corpse. He argues that body bequests relied on a kind of legal somatics or somatechnics in the use of the body, through which alternate imaginaries of the corpse, and attendant normative understandings, were visualised and instituted. By doing so, testators, executors and dissectors sought to affirm the law as they thought it should be, by acting as if it already were the law.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.071 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".