Platform Strategy in a Technopolitical War: The Failure (and Success) of Facebook Watch
Bibliographic record
Abstract
In recent years, as more corporations have decided to launch their own streaming platforms, it has become of greater importance for each to differentiate themselves through a suite of strategies intended to mark their place within the market. Facebook Watch, a video-on-demand service, is this paper’s case study, a unique example of a streaming effort undertaken by a technology company with an approach based in data collection and infrastructural might. Watch has not premiered a new scripted series since August 2020, reflecting Facebook’s abandonment of narrative seriality through series like SKAM Austin and a more pointed investment in the streaming space as an instance of what I am calling experiential seriality, meant to lead users along certain trajectories rather than building a reputation on its content. I argue that this is instead a move to compete with Alphabet Inc. (which owns YouTube) to dominate the world of online video, drawing Facebook users through links, recommendations, and other breadcrumbs to maintain continuous use.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".