New Visual Media and Gender: A Content, Visual, and Audience Analysis of YouTube Vlogs
Bibliographic record
Abstract
This study analyzes short vlogs posted to YouTube in order to investigate how and why people communicate using vlogs, and how viewers react to vlogs. Vlogs are of particular interest because they are visual texts that are user-generated. Vloggers engage with videos on several levels - they are both the encoders and the decoders, both the producers and audience of videos. In particular, we examine gender differences in creating vlogs, viewing vlogs and using YouTube. Our study interprets the dominant messages conveyed by the visual elements of vlogs. Analyzing online videos presents a new challenge for researchers: traditionally, analysis of visual media and communication focused on either the production or the reception of the material. Our vlog study uses a dual analytical approach to analyze both production and reception, while conducting content visual and audience analysis, thus making a useful contribution to the field of new visual media and communication.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".