#Headlesspreggos: challenging visual imaginaries of pregnancy and reproduction
Bibliographic record
Abstract
Amid new abortion restrictions in the USA, scientific advances in genetic technologies and investigations of COVID-19 vaccinations in pregnancy, news stories about reproduction abound, often accompanied by images of what journalist Josie Glausiusz has called the "headless, legless, pregnancy bump". These images of disembodied pregnant torsos at once improve search engine optimisation for news organisations while perpetuating the view of the 'bump' as the quintessential visual representation of pregnancy.The images that accompany news articles convey meaning beyond what is included in the text and work to reinforce stereotypes about race, gender and age. In the so-called obesity epidemic, for example, psychotherapist and fat activist Charlotte Cooper documented how images of fat people with their heads cropped out view had become a visual symbol of abjection-'the headless fatty'-without a face or agency to speak of. The use of 'headless preggos' similarly divorces pregnant people from the embodied experience of their pregnancies, reducing them to a single body part.In this article, we chronicle our experiences tracking images of headless preggos via Twitter, arguing that their use works to erase pregnant people's autonomy and to construct the fetus as the central concern in reproductive interventions. We begin by tracing the evolution of visual representations of pregnancy including the increasing focus on the fetus and 'bump'. We then provide a description of our experience with the Twitter account, including our exchanges with academics, journalists and others that highlight how the continued reliance on headless preggos obscures the experiences of pregnant people by focusing all attention on the fetus, as well as how the same images might be thoughtfully deployed. We conclude by offering suggestions for those creating and selecting images that might result in more robust, creative visual representations of pregnancy and reproduction.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".