Searching for Oneself on YouTube
Bibliographic record
Abstract
YouTube is the preferred online platform for today’s teenagers. As such, this article explores the relationship between socialization processes in adolescent peer culture and the meanings behind the production and reception of YouTube videos by teenage audiences. Two fields of enquiry comprise the data analyzed in this article. First, through content analysis, we studied the production of videos on YouTube by teenagers between the ages of 14 and 18. The discursive construction of an audience is expressed by YouTubers through intimate identity performances using specific, dialogical, and conversational modes. The second study investigated the reception of these videos by teenagers between the ages of 12 and 19 through the use of focus groups and in-depth interviews. The results explained the way young people develop a sense of closeness with YouTubers. When examined collectively, our studies reveal how teenage YouTube practices, both as production and reception of content, constitute a twofold social recognition process that incorporates a capacity to recognize oneself in others—like figures with whom one can identify with—and a need to be recognized by others as beings of value. The “intimate confessional production format,” as we have termed it, reinforces this bond.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".