Femvertising or femwashing? Women's perceptions of authenticity
Bibliographic record
Abstract
Abstract Stereotypes in advertising are recognized as contributing to the perpetuation of inequalities. In response to this, femvertising—“ advertising that employs pro‐female talents , messages , and imagery to empower women and girls ” (SheKnows, 2014)—is increasingly observed in the marketplace. Despite femvertising's prevalence, current research has failed to identify women's core understanding of it. The objectives of this research are to conceptualize femvertising from a consumer perspective, explore the nature of authentic femvertising, and differentiate it from femwashing. In‐depth interviews were conducted with 17 women. The findings help uncover femvertising's complex meaning as perceived by consumers and distinguish it from femwashing. The results suggest that the concepts of femvertising and femwashing coexist in consumers' minds. Six dimensions of authentic femvertising are identified: transparency, consistency, identification, diversity, respect, and challenging stereotypes. This research contributes to the consumer research, advertising, and branding literature; encourages a broader societal reflection about gender stereotypes; and offers several managerial implications.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".