The Commodification of the Body Positive Movement on Instagram
Bibliographic record
Abstract
Since 2012 there has been a heightened presence of the body positive movement on Instagram. Women who occupy non-normative bodies use the platform to post selfies to challenge dominant ideals of feminine beauty, including the demands to produce smooth skin, adhere to body size norms, and avoid bodily fluids. This has been accompanied by a barrage of media outlets advising their readers on the top body positive accounts they need in their life to boost their body confidence, and how to be body positive on Instagram for more self-love (Irish Examiner, 2016; Burke 2015; Vino, 2015; O'Reilly, 2016). News media circulated articles across social media platforms with stories heralding women who, through the use of selfies, open up about their experiences with eating disorders, shut body shamers down, challenge "bikini body" myths, and confront expectations directed at women's post-pregnancy bodies. Women who share the same experiences of and frustration with dominant ideals of femininity have identified with and participated in this movement through the use of body-positive hash tags, captions, and subject matter. However, as the popularity of the body positive movement and the influence of advocates grew, corporations began commoditizing the body positive advocates and using their influence to push products, capitalizing off of the movement. During the commodification process, the body positive advocates lose sight of their purpose and reproduce dominant capitalist ideologies, objectify their own bodies, and accept beauty modification 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".