Bibliographic record
Abstract
In recent years, a growing list of content creators have published videos announcing that they are leaving YouTube, taking a break, or reducing their upload schedule (Alexander). Many of these young creators state that their decision resulted from burnout, caused by a relentless schedule and obsession with YouTube’s algorithm and analytics— tools essential to success on this highly competitive creative platform (Srnicek). Its opaque algorithm, however, induces anxiety; an affect described as that which “arises when the subject is confronted by the desire of the Other and does not know what object he is for that desire” (Evans 12). Here, the Other is conflated as both audience and algorithm, insofar as what videos trend or are recommended is a complex merging of user-engagement and vetting by the algorithm. Attempting to discover the desire of the Other, creators examine data to speculate about what content will gain the most views. While some creators opt to chase trends and use clickbait titles, others avoid the algorithm’s detection and suppression by omitting key words known to be flagged, while others embrace defeat and instead drive their channels using drama and negative affect (Berryman and Kavka). Drawing on Lacan’s psychoanalytic clinical structures and theory of anxiety, this article examines how each of these approaches to navigating the platform represents a neurotic and sometimes perverse response to the algorithmic Other that is influenced by a neoliberal notion of creativity that privileges growth over its socially transformative power (Mould).
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".