Bibliographic record
Abstract
This paper explores the contributions of autonomist Marxist theory to my understanding of reality television, self-branding and social media. Autonomist Marxist ideas help to bridge the classic media studies divide between critical political economy and cultural studies, illuminating the very material connections between television’s mode of production, its texts, and its broader cultural context and impact. Concepts such as the social factory, immaterial labour, the socialized worker, and virtuosity, contributed by thinkers such as Mauricio Lazzarato, Paolo Virno, Antonio Negri and Michael Hardt, have enabled me to argue that reality television is a privileged site of production in the post-Fordist era; it not only produces texts or ideologies about work and life, but also models the monetization of “being” and produces “branded selves”. While autonomist ideas are extremely useful, the field of thinking is complex and not without its internal debates. This paper also explores contributions by George Caffentzis, Massimo de Angelis and David Harvie, specifically the concept of the war over measure, arguing that this concept helps to frame some of the ways in which the public expression of opinion and feeling online and in social media are being captured, measured and put to work for capital.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".