TV plus what? data-capital, brand extension, and lock-in effects in the Apple ecosystem
Bibliographic record
Abstract
Beyond general claims about Apple TV+ as a loss-leader for device sales, this article argues that Apple’s now vertically integrated TV strategy also seeks to (1) expand streams of behavioural data within its services strategy and broader operations; (2) extend its brand through the cultural industries of film and television; and (3) strengthen the lock-in effects of the company’s ecosystem of hardware, software, and services by selling visions of seamless integration, convenience, and connectivity. In examining these other uses and values of television for Apple, I call critical attention to the growing importance of the ecosystem metaphor for conceptualizing the complexity, dynamism, and scale of big tech firms and their diverse operations, of which platform television is increasingly included. Such an analysis begins to paint a fuller portrait of the neglected case of Apple in television and media industry studies, as well as the under-examined role of television in studies of Apple.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".