Bibliographic record
Abstract
Motion pictures are made, not mass produced, requiring a remarkable collection of skills, self-discipline, and sociality—all of which are sources of enormous pride among Hollywood’s craft and creative workers. The interviews collected here showcase the pleasures that attract people to careers in film and television. They also reflect critically on changes in the workplace brought about by corporate conglomeration and globalization. Rather than offer publicity-friendly anecdotes by marquee celebrities, Voices of Labor presents off-screen observations about the everyday realities of Global Hollywood. Ranging across job categories—from showrunner to make-up artist to location manager—this collection features voices of labor from Los Angeles, Atlanta, Prague, and Vancouver. Together they show how abstract concepts like conglomeration, financialization, and globalization are crucial tools for understanding contemporary Hollywood and for reflecting more generally on changes and challenges in the screen media workplace and our culture at large. “Essential reading for anyone interested in how Hollywood actually works.” -RAMON LOBATO, author of Shadow Economies of Cinema “Michael Curtin and Kevin Sanson craft a powerful elegy for organized labor, demonstrating how critical theory is sung to the everyday rhythms of the workplace.” -VICKI MAYER, author of Almost Hollywood, Nearly New Orleans: The Lure of the Local Film Economy “A star-studded cast with diverse talents, backgrounds, and perspectives tells a varied but consistent tale of the importance of organized labor and the challenges it faces when pitted against the forces of media consolidation and globalization, all set in that magical company town known as Hollywood.” -PATRIC M. VERRONE, writer and producer, former president, Writers Guild of America, West MICHAEL CURTIN is Duncan and Suzanne Mellichamp Professor of Film and Media Studies and director of the Global Dynamics Initiative at University of California, Santa Barbara. KEVIN SANSON is a senior lecturer in the Creative Industries Faculty at Queensland University of Technology and managing editor of Media Industries.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".