A Room in the Film Capital: The Social Economy of Lodging and Urban Change in Hollywood during the 1930s
Bibliographic record
Abstract
In Central Hollywood, during the 1930s, population became denser, housing values declined, and rooming houses increasingly defined much of the neighborhood. The lodging population was mostly white and native-born (but with significant Asian minorities), young, transient, and maritally unattached. They constituted a working class that was often precariously employed in the entertainment industry, the service sector, and other unskilled or semi-skilled occupations. The rooming houses and residential hotels provided the possibility of housing for the unemployed, the poorly paid, the temporary resident, and the elderly. Lodgers formed part of a socially diverse population in a neighborhood that offered opportunities for employment, services, and entertainment, usually within walking distance. During a decade when migration to Los Angeles was still considerable, rooming houses provided a flexibility in housing possibilities that would decline after the Second World War.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".