Young women's dialectical responses to fitspiration and thinspiration: A qualitative study
Bibliographic record
Abstract
With the popularity of image-based social media platforms like Instagram, researchers have begun to study relationships between social media and body image. Much of this research has used quantitative research methods, which cannot capture the rich, inner experiences of individuals. Given the dearth of qualitative research on what young women internally experience when viewing "Thinspiration" or "Fitspiration," this study used the consensual qualitative research (CQR) method, with the aim of capturing in their own words what young women feel and think in response to viewing fitspiration and thinspiration. Twelve young women viewed Fitspiration and Thinspiration profiles and were interviewed about these and similar everyday experiences. The CQR analytic team identified 10 general, 18 typical, and 11 variant domains (i.e., topic areas). An example general domain was "body image," under which the category "body dissatisfaction," fell; by viewing Thinspiration or Fitspiration, these young women were reminded how they are dissatisfied with how they see their own body or were prompted to believe others are reminded of this when viewing such content. An example typical domain was "diet/disordered eating"; several participants associated Thinspiration or Fitspiration with diet or disordered eating. From five of the 10 general domains emerged what we call an emergent dialectical theory of social media and body image. When viewing Thinspiration or Fitspiration, young women often seemed to feel or think two seemingly opposite things simultaneously within the topic areas of attainability, emotional reactions, social comparison, body image, and health.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".