Inclusive or Exclusive: Body Positive Communication in Imagery and Clothing in Athens, Greece
Bibliographic record
Abstract
It is expected that 40% of adults in Greece will be obese by 2030, and more and more individuals in the country are inactive. There are many reasons for obesity and inactivity; however, factors such as societal influences and appearance-focused communication are known to have an effect. Negative body communication—in other words, size-discrimination or shaming—may cause a person to consume unhealthy or large amounts of food and avoid exercise. Likewise, a system of social marking divides one group, the “ideal” group, from another group, the “lesser” group, thereby creating a perception of abnormality towards the “lesser” group and strengthening a social divide. Moreover, labelling theory states that individuals tend to behave based on the label assigned to them. In contrast, body positive communication seeks to challenge beauty standards and encourage a healthy mindset that in turn inspires healthy consumption and activity. This study analyzes communication towards females in Athens, Greece, through imagery, by examining front-of-store signage and mannequins, and clothing, by reviewing the size range available for purchase in stores. Major findings reveal that images do not show diversified sizing and the most common sizes are medium and small. This paper shows that negative communication could potentially exacerbate the overweight and obesity rate and that Athens, Greece has inadequate body positive communication practices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".