“Sur le web, je regarde des vidéos, des séries et des émissions”: catégorisation et sélection des contenus de divertissement visionnés en ligne par les jeunes de 12 à 25 ans
Bibliographic record
Abstract
More and more adolescents and young adults watch entertainment (movies, television series, web series, and videos) on the Internet. This trend seems linked to the growth in available content, to the profound changes taking place within the cultural industries, and to the rapid development of mobile devices. The transformation of the audio-visual landscape is causing formats, genres and categories traditionally associated with conventional television content to evolve. \n \nThis empirical study’s purpose, then, is to understand how youth audiences (teenagers and young adults) experience these new online viewing practices and how they categorize and describe them. What content are they watching on the Internet for entertainment purposes? How much time do they spend watching audio-visual entertainment content online? How do they label the online content that they watch? What place does classic television viewing still hold? And lastly, do these processes vary by age and gender? \n \nOur analysis was based on an exploratory qualitative design. We held 10 focus groups with young Quebecois people aged 12 to 25 (28 girls/women and 33 boys/men) from diverse cultural and social backgrounds. Our findings indicate that the Internet is becoming the primary viewing source for entertainment, especially among young adults. We found that the time spent online varies according to gender and age. Furthermore, adolescents and young adults watch a wide-ranging pool of online audiovisual content, with different things at different times of day, as the participants naturally create personal viewing schedules. Adolescents distinguish between two key forms of online entertainment content: long formats chiefly comprising original television content (reality shows, dramas, sitcoms and films) and shorter formats more suited to original web productions and accessed usually via YouTubeor Facebook. Most of the young people questioned feel no need to categorise the content they watch online beyond liking or disliking it. We found, however, that some young people, especially young adults, systematize content into multiple diverse categories based on classic fiction classifications, context of viewing (where and with whom), enjoyment levels (moderate or intense) and attributes relating to the websites used to access it or the YouTube channels’ names.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".