Black Gold, White Power: Mapping Oil, Real Estate, and Racial Segregation in the Los Angeles Basin, 1900-1939
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".