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Record W2790215856 · doi:10.17351/ests2018.212

Black Gold, White Power: Mapping Oil, Real Estate, and Racial Segregation in the Los Angeles Basin, 1900-1939

2018· article· en· W2790215856 on OpenAlexfundno aff
Daniel G. Cumming

Bibliographic record

VenueEngaging Science Technology and Society · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
FundersYork University
KeywordsCapitalismReal estateMetropolitan areaBoomUnderwritingPoliticsResource (disambiguation)EconomyPower (physics)Political scienceHistoryBusinessEconomicsArchaeologyLawFinanceEngineering

Abstract

fetched live from OpenAlex

In 1923, Southern California produced over twenty percent of the world’s oil. At the epicenter of an oil boom from 1892 to the 1930s, Los Angeles grew into the nation’s fifth largest city. By the end of the rush, it had also become one of the most racially segregated cities in the country. Historians have overlooked the relationship between industrialists drilling for oil and real estate developers codifying a racist housing market, namely through “redlining” maps and mortgage lending. While redlining is typically understood as a problem of horizontal territory, this paper argues that the mapping of the underground—the location and volume of subterranean oil fields, in particular—was a crucial technique in underwriting urban apartheid. Mapping technologies linked oil exploitation with restrictive property rights, constructing oil as a resource and vertically engineering a racialized housing market. By focusing on petro-industrialization interlocked with segregationist housing, this article reveals an unexamined chapter in Los Angeles’s history of resource exploitation and racial capitalism. Moreover, it contributes to a growing literature on the social production of resources, extractive technology and political exclusion, and the technoscientific practices used by states and corporations to mine the underground while constructing metropolitan inequality above ground.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.214
Teacher spread0.207 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEngaging Science Technology and SocietySame topicAmerican Environmental and Regional HistoryFrench-language works237,207