“I Believe in Santa Claus” and Ozempic: A Foucauldian Discourse Analysis of Holiday Health Advertising
Bibliographic record
Abstract
This study examines the weight-related discourses in holiday advertising for Ozempic, a prescription drug originally developed for diabetes management but now widely marketed for weight loss. Sponsored Facebook advertisements for Ozempic were collected throughout December 2024, with 12 ads analyzed through Foucauldian discourse analysis. This analysis identifies three interrelated discursive constructs: (1) Santa Takes Ozempic, (2) Ozempic as the Perfect Holiday Gift, and (3) Medical Authority Meets Holiday Cheer. These advertisements use cultural symbols like Santa Claus and New Year's resolutions messaging to (re)produce dominant and contested discourses about fatness and weight loss, while constructing pharmaceutical intervention as both a necessity and a gift. The analysis highlights how these marketing strategies mobilize biopower, construct self-surveillance as normative, and contribute to the commodification of health, reinforcing weight stigma under the guise of holiday celebration.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".