Bibliographic record
Abstract
This research paper traces the history of how the scientific measurement and visualization of sexed and racialized bodies enable them to be regulated in the context of abortion. In Part I, the paper examines how scientific visualization techniques developed in the 19th century prompted the Roman Catholic Church to declare any abortion following conception a sin, despite abortion being previously accepted if it occurred within two to three months of fertilization. This idea is then connected to how anti-abortion protestors use visual rhetoric to flatten narratives about why people seek abortions. They use singular images of aborted fetuses as visual substitutes for complex personal circumstances, and (like the Catholic Church) use these images to present the fetus as a fully formed human being. In Part II, the paper characterizes such visualization techniques as a type of rationalization, the process of turning something (like the body) into units of information, which enables it to be controlled. Part III studies how period-tracking apps rationalize the menstruating body and are used as evidence to penalize individuals for having abortions; however, the data obtained from these apps may be inaccurate because the human body is a complex biological organism that is inherently unpredictable (meaning that it can never be fully rationalized). Part IV examines how rationalization methods are not neutral and draws upon case studies of racialized individuals convicted of having abortions to argue that racialized bodies are perceived as criminal even before information is collected about them.
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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 | 0.005 | 0.015 |
| 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 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".