Putting the YouTuber Front and Center: Organizational Dynamics on Online Platforms
Bibliographic record
Abstract
Platforms present novel ways of organizing labor, and social media platforms are no exception. Social media platforms have transformed cultural production by bringing together multiple actors, shaping their interactions, and enabling commercialization of user influence and activities. One such platform, YouTube, is a digital content-sharing platform with massive social and economic impact and a site of rich social and cultural processes that are of relevance to organizational scholars. The technological tools on YouTube, such as algorithm-based recommendations, comments, livestreams with chats, and data analytics monitoring, amplify the effects of audiences and algorithms in ways that require the adaptation of existing theories and development of new theories in management scholarship. To illustrate the diversity of processes and empirical contexts on YouTube, we bring together four presentations that ask: 1. How do platforms shape cultural producers’ interactions with their audiences and the role of audience expectations in their work? 2. How do platform algorithms and professional norms interact to affect cultural producers’ meaning-making and practices over their career trajectory? 3. How do cultural producers relying on community ties balance community demands with commercialization? 4. How do platforms allow evaluators of cultural products to amplify social movements? These presentations highlight different tensions that arise between the force of commercialization and other social values – community relationships, authenticity, diversity, and professional norms – as platforms introduce new ways of organizing. They contribute to literatures in organizational theory and economic sociology on the platform economy, cultural production, social movements, authenticity, algorithmic management, entrepreneurship, and evaluations. Traffic Sources: Audience Conventions and Content Creation on YouTube Author: Matthew Rafalow; Google Inc and U. of Southern California Metrics to Rule All?: Worker Reactivity to Metric-based Control and Alternative Source of Feedback Author: Yun Ha Cho; U. of Michigan Just between us: Sustaining online community amidst increasingly commercialized participation Author: Njoke Thomas; Boston College Cultural Gatekeepers as Activists: Relational Policing of Authentic Representation Author: Youjin Jenna Song; Northwestern Kellogg School of Management Author: Brayden G. King; Northwestern U.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".