Bibliographic record
Abstract
Algorave is a global community dedicated to expanding the boundaries of algorithms and coding in the context of live electronic music. Through algorithms, Algorave members have discovered the power of altering music’s structure. In the face of a fully automated future, this article queries whether this power may be directed towards defying political, economic, ideological, or ethical systems. First, I present Algorave as an idiosyncratic environment of a post-work society. Second, I develop a critique of Kathi Weeks’ handling of the concept of subjectivity to question a post-work imaginary that comprises the subject. Third, I explain the pertinence of a critical subjectivity praxis for Algorave to enrich their post-work stance, whereby I suggest using their analytical lens on algorithms to prevent subjectivity from passing on to the post-human terrain. From here, I conclude that the subject of automation is the automated subject, and that a post-work society is not possible without overthrowing subjectivity. I ultimately caution the advocates of automation when pursuing post-work, for if automation manages to make subjectivity a part of algorithms with governmental impact, we will be—now and for good—automatically condemned to living as subjects, significantly reinforcing the basis of neoliberal work.
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 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 | 0.001 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".