What Remains? Rethinking Feminist Theories of Pregnant Embodiment through the Symbolic Language and Lived Experience of Pregnancy Loss
Bibliographic record
Abstract
Abstract When people reach for symbolic language like metaphor in an effort to express their understanding of a complex experience like baby and pregnancy loss it is usually because everyday language is inadequate. We demonstrate how an analysis of people’s accounts of stillbirth, miscarriage and termination for fetal anomaly collected in England suggests a variety of explanations for “what remains” after pregnancy loss. These include bodily traces, ashes, symbolic objects, and social relationships: a range indicative of the different physical, material, and affective elements that inform how a person understands bodily and temporal separation after pregnancy loss. We engage with the work of feminist philosophers who critique models of pregnant embodiment; those who propose parthood, and theorists who argue for more complex models of intercorporeal relationality. We then discuss insights from linguistics about embodied cognition and metaphorical language. Paying critical attention to symbolic language, combined with feminist thinking about intercorporeality, offers a methodology for analyzing the embodied metaphors that people use when they talk about their experiences of pregnancy loss. These articulations about “what remains” support our claim that the intercorporeal model of embodiment should be extended spatially and temporally beyond the moment of physical separation following pregnancy loss.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.066 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".