“More time, more pain, more blood”: Creating accurate and diverse accounts of miscarriage in British TV and film
Bibliographic record
Abstract
Media plays a critical role in reflecting and shaping cultural values, norms and public understandings. Scholars have long noted that mass media not only reflects societal attitudes but also serves an educational function by informing viewers about topics such as health. This paper discusses how miscarriage is portrayed in British scripted TV and film and how academic research might influence this by documenting a collaborative project between an anthropologist and Women in Film and TV, a membership organization for women across various media professions. The project aimed to provide screenwriters with information to create more accurate and diverse accounts through a writer’s workshop. Prompted by ethnographic research projects focusing on miscarriage, the project involved conducting archival research into the portrayal of miscarriage in American, Canadian and British television and film. From this analysis, a video reel was curated highlighting typical representations of miscarriage. Opening the workshop, the reel was followed by presentations by two social scientists who work on miscarriage. The event included a panel discussion with the academics and National Health Service clinicians, including a consultant gynaecologist and two specialist early pregnancy nurse sonographers. Key themes, audience responses and implications for future research projects are explored.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".