Bibliographic record
Abstract
This article deals with the bodies of women and with gender. While some of my comments also apply to men's bodies (especially those from oppressed groups), and to violence in general, my assumption is that all bodies are gendered and that certain kinds of violence are also gendered. The violence implied in some of the snapshot accounts above is of this kind: gendered violence. It is enculturated and systemic violence directed against women simply because they are female bodies. Since others have enumerated the horrors of this kind of violence, it is not necessary to do so any further here.4 What I am concerned with is the role of the Christian tradition, not only in the past but also more importantly in the future, in addressing violence against the body, particularly female bodies. How we read the past has a lot to do with what we will create in the future. My contention is that both a more nuanced understanding of what constitutes "the body" and a more careful reading of some aspects of the Christian tradition can produce a foundation for a reconstructed theology of the body. It could be argued that since women were especially associated with the body in the body / soul dualism throughout the Christian West, the recovery of a theology of women's bodies is the critical starting point for any theology of the body.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".