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Record W7046580432

Crossing Americaâs Borders: Chinese Immigrants in the Southwesterns of the 1920s and 1930s

2017· article· en· W7046580432 on OpenAlexafffund

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of CanadaWilfrid Laurier University
KeywordsFrontierImmigrationOpposition (politics)MiracleChinaEconomic miracle
DOInot available

Abstract

fetched live from OpenAlex

Today, when we think of the film Western, we think of a genre dominated by Anglo-American heroes conquering the various struggles and obstacles that the nineteenth-century frontier presented to settlers and gunslingers alike—from the daunting terrain and inclement environment of deserts, mountains, and plains to the violent opposition posed by cattle ranchers and Native Americans. What we tend to forget, most likely because the most famous Westerns of the last seventy-five years also forgot, is that Chinese immigrants played an important role in that frontier history. As Edward Buscombe confirms, “[g]iven the importance of their contribution, particularly to the construction of the Central Pacific railroad, the Chinese are under-represented in the Western.” In the 1920s and ’30s, many films focused on the smuggling of illegal Chinese immigrants—whether men for work or women for prostitution. Although a topic for a handful of social dramas such as The Miracle Makers (W. S. Van Dyke, 1923), Speed Wild (Harry Garson, 1925), Let Women Alone (Paul Powell, 1925), Masked Emotions (David Butler/Kenneth Hawks, 1929), and Lazy River (George B. Seitz, 1934), as well as newspaper-crime films such as I Cover the Waterfront (James Cruze, 1933), and Yellow Cargo (Crane Wilbur, 1936), the smuggling of Chinese people was also common in Westerns. According to the AFI Catalog, Chinese characters and actors appear in minor roles in at least seventy-eight silent and classical-era Westerns as cooks, laundrymen, and restaurant owners. These seventy-eight Westerns ranged from A-Westerns set in the nineteenth-century frontier to B-Westerns set in the modern West. Of the latter group, several Westerns—more specifically, “Southwesterns”—offered plots that connected Chinese immigrants to crime through the smuggling of opium, laborers, and prostitutes into the United States from China via Mexico. Southwesterns were lower-budget and were typically shot with cheap sets, grainy film stock, and few retakes (what we would call today B-Westerns), set in the contemporaneous Southwest (i.e., California, Arizona, New Mexico, and Texas) near the border with Mexico: it was this borderland setting that invited stories of smuggling and border penetration.3 According to the AFI Catalog, of the 182 Westerns released between 1910 and 1960 that are set in the Mexican–American border region, 39 offer plots centered on smuggling various kinds of things, from opium and liquor to silver, dynamite, and counterfeit money, as well as guns. Southwesterns exploited the borderland setting to offer exciting smuggling plots that connected crime to Chinese immigrants. Although the opium-smuggling films connect crime to China, they rarely feature Chinese characters. In contrast, the immigrant-smuggling films present Chinese people as a physical and visible alien threat to America’s national borders, and it is these films that are the focus of this article. As this article will demonstrate, there were many Westerns centered on smuggling plots related to Chinese immigration, and the borders that these films were concerned with were as much cultural and racial as territorial. In other words, the presence of illegal Chinese immigrants assisted the genre of the Western to confirm the borders of American national identity.

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)0.001
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 score0.391
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.005
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.262
Teacher spread0.249 · 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
Published2017
Admission routes2
Has abstractyes

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