“How it actually works”: Algorithmic lore videos as market devices
Bibliographic record
Abstract
On YouTube, self-styled algorithmic experts claim to know how algorithms “actually work.” However, their knowledge is largely speculative. Developing recent work that pays attention to algorithmic expertise, I argue that algorithmic lore videos are “market devices” which are economically productive for the platform in four ways: they (1) legitimize platform narratives of algorithmic objectivity, (2) teach creators how to calculate the value of content and format it according to platform metrics, (3) encourage creators to build and govern audiences, and (4) justify continued content production even when it does not pay off for creators. Thus, despite claiming to describe “how the algorithm actually works,” algorithmic lore videos are performative; by teaching creators how to understand and act in the platform economy they do important work to bring the platform’s “visibility markets”—its labor market of content creators engaging in content production and its goods market of content to be watched—into being.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".