Deflecting immigration: networks, markets and regulation in Los Angeles * Ivan Light
Bibliographic record
Abstract
Immigration scholars are very familiar with the important place Ivan Light occupies with regard to immigration and ethnic enterprise, a field he was instrumental in identifying in the early 1970s. In Deflecting Immigration Light makes yet another significant contribution to the immigration debate in the USA, and his work may have some resonance with an international audience interested in urban growth and immigration. In a coherent, readable, and extremely persuasive book, Light suggests that municipalities are effectively creating national immigration policy in the USA, and that policy is one of sequential immigrant absorption and deflection shaped by both macro-economic conditions and local political decision making. Light bases his argument on primary and secondary sources with an emphasis on Mexican immigration to Los Angeles. Half of the chapters are articles previously published elsewhere, but the entire book reads fairly seamlessly, although a chapter on Asian place entrepreneurs does seem somewhat misplaced. Light's main critique is of globalization theorists who see economic restructuring driving the demand for low income labour, to which low skilled immigrants respond. Light counters this demand driven model with the case of Los Angeles, arguing that as long as the economy grows demand driven migration continues, but, as the supply of immigrants surpasses the capacity of the formal economy to absorb them, immigration becomes supply or network driven.
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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 | 0.002 | 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".