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Record W4392109944 · doi:10.1177/00961442241227130

A Room in the Film Capital: The Social Economy of Lodging and Urban Change in Hollywood during the 1930s

2024· article· en· W4392109944 on OpenAlexaff
F. R. Bode

Bibliographic record

VenueJournal of Urban History · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsHollywoodCapital (architecture)Social capitalEconomicsEconomic geographyEconomyMarket economySociologyHistorySocial scienceArtVisual artsArt history

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.009
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.032
GPT teacher head0.215
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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