#Emotional: Exploitation & Burnout in Creator Culture
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
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.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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