Diversity in Femvertising: An Experimental Investigation
Bibliographic record
Abstract
ABSTRACT Diversity in femvertising—advertising that empowers women through inclusive representation—has gained significant traction in recent times. Yet consumer perceptions of its authenticity and effectiveness remain underexplored. This study examines the impact of diverse representation in femvertising on brand attitudes, purchase intentions, and consumer behavior. Findings from four experimental studies reveal that diversity enhances brand perceptions and purchase intentions, mediated by perceived brand authenticity. However, political orientation moderates these effects; liberals respond more positively to diverse advertisements while conservatives prefer homogeneous representations. These insights highlight the importance of authenticity in femvertising and the potential risks of tokenism and political polarization. The research contributes to advertising knowledge by incorporating intersectionality, examining behavioral outcomes, and addressing the ideological divide in consumer responses. Practical implications suggest that brands should balance diversity with authenticity to foster inclusivity without alienating key audiences. Future research should explore the optimal level of diversity, long‐term effects on brand loyalty, and the role of AI‐driven personalization in diverse advertising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".