Deconstructing Menvertising Stereotypes: A Systematic Review, Research Agenda and Practical Implications
Bibliographic record
Abstract
ABSTRACT Since the #MeToo movement of 2017, consumers have been looking for more diversity and inclusion in their world. As a result, advertisers are implementing strategies such as femvertising and even menvertising to win over this more inclusion‐oriented audience. A large number of studies have focused on women, particularly representations of women in femvertising . The aim of this article is to develop an understanding of gender representation by filling in the gaps in existing studies on menvertising . With a view to achieving equity for all people and to better understand the evolution of gender through advertising representations, we conducted a systematic literature review of 236 articles, including lexicometric, bibliometric and in‐depth expert interviews analyses, focusing on masculinity post‐#MeToo and its representation in advertising. We identified various theories and concepts that better enable us to understand the evolution of menvertising , and we developed an initial conceptual model of menvertising . Finally, this study proposes a research agenda to guide researchers in pursuing studies on menvertising .
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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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".