Bibliographic record
Abstract
In keeping with this issue of Alternate Routes’ focus on forms of low-waged and no-waged work, this article focuses on low-waged and non-waged work in the reality TV production sector. How do reality-TV studios try to maximize profits by keeping the costs of making their commodities to a minimum, and how does the push for profit disorganize and devalue labour? This article contextualizes and critiques how reality TV studios try to maximize profits by minimizing production costs in three sections. “Reality TV Producers: Work Behind the Scenes” shows how reality TV’s classification as “non-scripted” programming enables production companies to exploit a non-unionized workforce. “Reality TV Celebrities: Work in the Scenes” highlights how reality TV production companies exploit the no-waged labour of “contestant-participants.” “Reality TV Interns: Work Behind the Scenes, and In Them” shows how studios use internship programs to get workers to make reality TV programs without pay and how some of these programs glorify no-waged work. The article concludes on a more optimistic note with an overview of reality-TV worker challenges to reality-TV’s owners with unionization, strikes, litigation, publicity and discourse.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".