Algorithms and taste-making: Exposing the Netflix Recommender System's operational logics
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
As the Streaming Wars continue to heat up, recommendation systems like the Netflix Recommender System (NRS) will become key competitive features for every major over-the-top video streamer. As a result, film and television production and consumption will increasingly be in the hands of semi-autonomous algorithmic technologies. But how do recommendation systems like the NRS work? What purposes do they serve? And what sorts of impacts are they having on film and television culture? To respond to these questions, this article will (1) examine how algorithms are impacting processes of taste-making and (2) re-evaluate some of the critical theoretical perspectives that have come to dominate the discourse surrounding algorithmic cultures. To do so, I join Bucher ((2016) Neither black nor box: Ways of knowing algorithms. In: S Kubitscko and A Kaun (eds) Innovative Methods in Media and Communication Research. Cham: Springer International Publishing, pp. 81–98; (2018) If…then: Algorithmic Power and Politics. London: Oxford University Press) in adopting a relational materialist perspective of algorithms and proceed to reverse engineer the NRS; an experiment that exposes the system’s circular and economic logics while highlighting the complex and networked nature of taste-making in the film and television industry.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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