Deconstructing Menvertising Stereotypes: A Systematic Review, Research Agenda and Practical Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| 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 it