Investigating Cinephile SVoD Catalogues with Small-Scale and Cobbled Together Methods
Bibliographic record
Abstract
This article considers strategies and tools (qualitative methods, web scraping, and small-scale data visualization) that can be used to study cinephile video-on-demand (VoD) services, such as BFI Player, Fandor, Filmatique, FilmStruck, LaCinetek, Mubi, Sundance Now, Tënk, and The Criterion Channel. It argues that these cinephile VoD services have characteristics that require a distinctive approach to data collection and analysis. The metaphor of cobbling, which emphasizes the heterogeneity of borrowings from both academic and nonacademic practices, is developed throughout the article. The goal is not so much to present a streamlined methodology as to reflect on the choices and adjustments made to create a unique set of analytical strategies. The article begins by describing the steps taken to achieve a multimodal analysis of the catalogs’ websites and the circulation of content and subscribers through them, before moving on to consider the development of specific methods for collecting and visualizing data on title selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".