Industrial Organization of Online Video on Demand Platforms in North America: Between Diversity and Concentration
Bibliographic record
Abstract
This article examines online video on demand platforms as a new dissemination window for audio-visual content in North America, specifically, in the less-known cases of Mexico and Canada. The comparative study describes and discusses the markets and industrial organization of these subsectors and highlights the common and distinctive characteristics of their ecologies vis a vis United States’ strong hold over the screen content industry in the region. The main line of inquiry is—what is the contribution of this new window regarding the provision of a more diverse screen outlet ecology in the current context of concentration in the audiovisual sector? In this way, the article connects with the long-standing debate between optimistic and skeptical accounts of whether digital technologies can disrupt traditional concentration tendencies in the cultural industries. To answer the research question, new platform environments were mapped and classified to produce original statistics. These results were compared with official and market statistics; document analysis of news, business and government reports. The article argues that asymmetries in the three countries’ screen landscape across the region have been carried over to the new dissemination window and that outlet diversity does not translate into exposure diversity. The article also empirically monitors challenges to the availability of diverse content providers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".