New Feminist Studies in Audiovisual Industries | Employer Femvertising: Women Empowerment in Employer Brand Messages
Bibliographic record
Abstract
This article develops for the first time the concept of employer femvertising as a talent attraction tool companies can use to position themselves as employers committed to empowering women. Based on the concepts of employer branding and femvertising, employer femvertising emerges as a set of messages and strategies through which an organization positions itself as an employer that demands and promotes the professional development of women and fights the stereotypes, obstacles, and labor challenges women face in the workplace. A social media content analysis was conducted to explore the messages used in the employer branding campaigns published by four top employer companies on LinkedIn in the first quarter of 2020. The contributions in this article reveal the existence of employer femvertising in employer brand building and identify a taxonomy of 5 categories for this femvertising content: gender equality, female leadership, inclusive recruitment, female talent growth, and work-life balance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".