Education and migration in a border city
Bibliographic record
Abstract
Major demographic processes (fertility, mortality and migration) are both causes and consequences of future and previous processes. Demographers in border locations have special methodological challenges and usually face more rapidly changing dynamics than other locations. Migration is a process most difficult to both measure and understand; unlike birth and death, it does not happen to everyone, but is selective. Age, education, health, wealth, occupation, gender and family composition all contribute to making a person more or less likely to move from one place to another or remain where they are, as are a host of other factors. This study attempts to sort out some of these factors, using primarily Census data from 1990 and 2000. We began with the local concern that El Paso's persistently low indices of education and income result from an outflow of the more educated segments of the community. However, our findings suggest that the relationship between migration and education is not linear. Moreover, other variables such a language, birthplace, gender and ethnicity appear to be as or more important in predicting whether a person will stay or leave than educational attainment. The characteristics of new arrivals also affect the composition of a population at the aggregate level. As the population of Hispanics disperse throughout the country, a process rapidly underway as evidenced by the 2000 Census, our findings may have implications for many other communities. A more complete understanding of the causes and consequences of migration will require a combination of both qualitative and quantitative analysis.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".