John, a 20-year-old Boston native with a great sense of humour: on the spectacularization of the self and the incorporation of identity in the age of reality television
Bibliographic record
Abstract
Reality television programming simultaneously narrates the conditions of the social factory and produces new forms of labour in and through them. This essay explores the nature of the labour performed by the shows' participants and argues that it involves the self-conscious development and management of public persona based on templates of the self supplied by corporate media culture. This labour of self-presentation operates simultaneously as work for the television industry and as a form of image-entrepreneurship for the individual participants. Insofar as this form of labour involves the alienation of embodied subjectivity into image commodities with recognizable market value, it constitutes a form of self-spectacularization. Reality television programming also provides templates for these spectacular selves within a distinct corporate culture, which aims to contain and control individuals' virtuosity, thus incorporating identity. The Apprentice and Joe Schmo are explored as examples of reality shows that dramatize and embody the collapse of any meaningful distinction between notions of the self and capitalist processes of production. This process of both narrating and producing a branded self enacted by the reality television might be seen as part of a broader multi-level marketing campaign we could call the corporate colonization of the real.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".