From Lip Smackers to Wrinkle Cream: Priming the Next Generation of Consuming Women
Bibliographic record
Abstract
The purpose of this research was to determine if there is a model of ideal femininity communicated through advertising in girls’ and women’s magazines. To assess the representations of women in magazine advertisements, a content analysis of advertisements appearing in three top-selling, demographically-defined women’s magazines (Girls’ Life, Seventeen, and Cosmopolitan) was conducted. Using feminist theory and hegemony theory as critical lenses, advertisements were analyzed quantitatively and qualitatively. Each advertisement was assessed using five criteria: physical characteristics, social context, personality and attitude, and subtext. Using this data to establish the dominant representations of women, it was determined that there is a model of ideal femininity which is developed through establishing common ideals shared by all three magazines and by gradually introducing new ideals which correspond to shifts in real-world interests and experiences of women. It was concluded that a model of ideal femininity is developed through advertising in girls’ and women’s magazines, this model is used as a guide to direct girls and women towards specific ideal preferences, attitudes and behaviours, and this model continues to emphasise traditional cultural values and gender ideals which are not necessarily reflective of the range of roles women assume in today’s society.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".