Bibliographic record
Abstract
Giant media corporations are the representation of the ruling class who have the power to control media content published to the public and the right to disseminate their preferred ideologies to society. People outside the In-Group are considered media consumers who are repeatedly exposed to media productions containing hegemonic ideologies. Consequently, due to the daily exposure to hegemonic ideas incorporated in traditional and new media, media consumers began to accept hegemonic ideas as social norms. That is when media hegemony actually happens. The idea of the hegemonic male gaze can usually be found in all kinds of business promotions used in the media industry, principally in advertisements. Since males are not the only groups of people who are eager to see images of hot girls in media productions, the sexualized performance of women in advertisements is also attractive to females. After perceiving a certain beauty standard and aesthetic trend shaped by the hegemonic male gaze in social media, women are more likely to pursue the idealized definition of beauty by altering their physical features. Beauty filters on social media are the most popular tool used by females to change their looks to fit into the current beauty standard. This article uses the use of beauty filters on Douyin (the Chinese version of TikTok) as an example to illustrate the cause-and-effect relationship between the hegemonic male gaze in advertisements and the prevalent aesthetic trends.
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.
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 | 0.027 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.048 | 0.035 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".