Gender Representations in Social Media and Formations of Masculinity
Bibliographic record
Abstract
Social media has become a primary socializer as it has the ability to shape the identity and perspectives of its users. Gender socialization is the process in which people learn and internalize norms and behaviours associated with their respective gender. Through their own social media platforms, influencers post representations of self that they feel represent themselves in the world. Since these influencers are popular, many adolescents are able to see and process these photos/videos as a part of their own process of identity formation. This study asks the question, how do male influencers demonstrate masculinity through their posts and comments? The literature reviewed for this study offers insight on the formation and representation of masculinity. Key concepts include hegemonic masculinity and defensive heterosexuality which aid in understanding masculinity as it is manifested in our society. The study sample includes ten male instagram influencers. Five photos were taken from their account that demonstrated representation of self and were each judged based on a set of criteria consisting of six factors. The results of this study show that these influencers are in fact demonstrating specific modes of masculinity through their photos which is consistent with how masculinity is portrayed in society today. However, these photos also demonstrate that some male influencers are shifting away from patriarchal forms of masculinity and are showing more interest in grooming and fashion, therefore highlighting a metrosexual mode of masculinity.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".