Augmenting Content Analysis in the Era of Streaming Video: Harnessing AI for Comprehensive VoD Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it