Girls on YouTube: Gender Politics and the Potential for a Public Sphere
Bibliographic record
Abstract
YouTube is a massively popular video streaming website. It has become so ingrained in daily consciousness that it is almost difficult to conceive of a time in which it did not exist. YouTube’s slogan is “Broadcast Yourself.” It connotes a sense of freedom to be whoever you want to be and communicate this conceptualization of the self with the world. Vlogs, or video blogs, share the same function as a traditional diary except there is no assumption of privacy since the videos are uploaded publicly. Both men and women participate in the production of these videos. However, the experience of male and female YouTubers is quite different. The following paper will explore whether YouTube operates as a public sphere in light of the gender divide that appears to have formed on the site. The four objectives of the paper are as follows: to define the concept of the public sphere, to determine the factors that have contributed to a gender divide on YouTube by analyzing the gendered use of the medium, to examine the reception of the controversial “Girls on YouTube” video by female vloggers, and to evaluate whether YouTube operates as a public sphere in light of the findings of the preceding sections. Ultimately, this paper will give greater insight into whether new media offers the possibility for women's voices to be heard or if it is simply a remediation of older patriarchal technology.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".