Social Media Opinion Leaders Who Cater to the Male Gaze and Their Influence on Beauty Standards: A Case Study on Kim Kardashian’s Posts on Instagram
Bibliographic record
Abstract
As the product of the long-lasting patriarchal social system, the male gaze is not only directing the gender performances on social media but also affecting females' behaviors in the display of beauty in real life. Social media and opinion leaders are shaping active online users' perceptions of the current beauty standard due to the audience's repeated exposure to those media content that shows feminine beauty ideals. The paper did a case study on Kim Kardashian's photos posted on Instagram using categorical sampling and data analysis as the research methods in the two surveys. This research proved the cater to the male gaze in photos posted by celebrities on social media and discusses the influence of social media opinion leaders on the audience's ideology and behavior regarding beauty standards. As a result, over-engaging with social networking platforms and images published by social media opinion leaders can lead to severe consequences on the audience, such as appearance anxiety and low self-esteem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".