Teaching every body: A critical analysis of school programming on body image
Bibliographic record
Abstract
Body dissatisfaction in children grows into harmful practices as they age. Schools provide education and programming to promote body satisfaction and positive body image in adolescents, but these teachings can be improved. This paper analyzes educational stakeholders’ services on body image through a critical lens while suggesting solutions to improve lessons, courses, and programming. Through braiding internal lessons with external programs, schools can fight against the potential risks of negative body image on adolescents. The literature review highlights the need for early education on body image and the importance of caregiver intervention. A critical review of the teacher and student dynamic introduces the opportunity that teachers as caregivers have to promote positive body image. Next, this paper discusses external intervention programs and the effectiveness of gender-specific programming while remaining critical of a lack of male-focused programs. This paper then discusses how teachers have more opportunities to hold open discussions for students to learn and share. Lastly, this paper describes how physical education classes can be modified to promote feelings of attractiveness and positivity while correcting misconceptions regarding exercising and gender. These changes to school programming will promote positive body image in students and open up classroom conversations.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".