Bibliographic record
Abstract
Using data from the American Community Survey 2005, 2006, and 2007 we quantify the socio-economic factors that determine the likelihood of being self-employed (SE) of Latinos and White non-Latinos in the Pacific North West, U.S., and how these factors affect their income. Only 5.5 % of Latinos are self-employed compared to 9.4% of White non-Latinos and Latinos earn 30 % less than White non-Latinos. Non-linear decomposition results show that age and educational attainment explain 41 % of the ethnic gap in the probability of being SE among the U.S. born. In contrast, gender, type of occupation, number of years in the United States, and good command of the English language explain 22 % of the ethnic gap in the probability of being SE among immigrants. Linear decomposition of self-employment income (SEI) shows that age, marital status, and type of occupation explains 90 % of the ethnic gap in SEI among the U.S. born; however, ethnic differences in SEI among immigrants are mixed. Thus, policies aimed to reduce the ethnic gap in SEI should take into account the skewed distribution of skills of Latinos, and the degree of transfer ability of immigrants ’ skills into the local environment. Reducing this gap poses the challenge of improving the skills of many self-employed Latino immigrants with limited choices or transferable experience.
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.781 | 0.673 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".