Bibliographic record
Abstract
This book examines the effectiveness and limitations of existing body image law, and proposes evidence-based regulatory alternatives informed by public health and psychology research. Poor body image affects millions of people worldwide, and, despite the body positivity movement, the pressure on women in Western countries to have smaller bodies continues to cause significant harm to many. This book contributes to improving this, through drawing on evidence from research which outlines the harm excessive social media use and exposure to models with smaller bodies can cause to an individual’s body image. It explores the regulatory efforts of governments in Israel, France and Norway which passed Body Image Laws, and failed attempts to pass bills in this area in Canada, the United Kingdom, Brazil and the United States. This book analyses the outcome of BMI requirements for catwalk fashion models, warning messages on digitally altered images and prosecuting pro-anorexia content creators. It asks why the current forms of body image law do not align with significant findings from public health and psychology. This book offers a compelling, evidence-based overview of body image as it intersects with law. It argues that body image law in its current form is unlikely to be effective and makes suggestions for evidence-based approaches instead. This book will be of interest to researchers concerned with body image and the law as it relates to public health law, social media law and advertising law and anyone who has poor body image, an eating disorder or knows someone who has.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".