Augmenting Content Analysis in the Era of Streaming Video: Harnessing AI for Comprehensive VoD Research
Bibliographic record
Abstract
Streaming services have profoundly transformed the audiovisual industry, reshaping both production and distribution practices as well as viewing habits. Notably, video-on-demand (VoD) services have greatly expanded the number of series produced annually. Yet despite the extensive volume of audiovisual productions available on VoD services, most scholarly work continues to prioritize case studies or limit their scope to a small corpus of texts. This article critically examines artificial intelligence (AI)-assisted content analysis as a methodological avenue that could allow scholars to analyze extensive corpuses of audiovisual productions available on streaming services. Using multimodal generative algorithms and other integrated digital tools, such as the large language model (LLM) Gemini and the platform Google AI Studio, we will show how AI-assisted analysis might enable more thorough understandings of media production within the VoD landscape. Drawing on the results of test analyses conducted with Gemini, this article also critically addresses the epistemological and methodological challenges of AI-augmented content analysis.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".