Streaming platforms’ contribution to capitalization of local audio-visual producers in Mexico and Canada
Bibliographic record
Abstract
This article examines the contribution of streaming platforms in providing financial investment – in the form of co-productions, commissions and acquisitions of audio-visual content – as well as capital returns to local audio-visual producers. It will focus on the North American region, particularly on Mexico and Canada, as gravitating around stronger US audio-visual companies. Studies of traditional audio-visual windows in the countries studied have pointed out the undercapitalization of independent content producers due to financial structures and capital return models that are disadvantageous to them. This article questions: what is streaming’s contribution, as a new commercialization window, to the capitalization of local independent producers? The research conducted a qualitative study of interviews with film producers and distributors as well as an industrial analysis based on previous studies, media and business reports. The research has found that streaming tends to provide: (1) equal or slightly less returns than what the DVD window used to offer; and (2) equal or more generous figures than those delivered by TV and cinema exhibition windows. Furthermore, streaming has promoted a burgeoning production activity – adding to the production from traditional players (film and TV). These are benefits that should not be overlooked. However, streaming has not altered independent producers’ disadvantageous position: (1) revenue shares are still relatively small; (2) licences represent small percentages of what content costs to make; (3) commissioning and co-production budgets are fairly close to production costs; and (4) the boom of platforms’ original production is actually a battle among large corporations to control intellectual property (IP). All the above keep hindering the financial capacity of local independent producers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".