Transformation of Latino Neighborhoods in the Tucson Metropolitan Area from 1990-2010
Bibliographic record
Abstract
abstract: Changes in Latino neighborhoods in Tucson, Arizona that occurred between 1990 and 2010 were studied. The overall Latino population increased substantially within the larger metropolitan area during the target time period. Neighborhoods were selected that had changed to become predominantly Latino during the target time period based on maps measuring ethnic clusters. Research was designed to characterize Latino neighborhoods in Tucson in terms of transformation. Methodology for comparison between changed and unchanged neighborhoods was developed. Observations were made in the three new neighborhoods, as well as in three historically Latino neighborhoods that experienced little change during the same time period. Interviews were conducted with residents from each neighborhood. Exploratory findings were made regarding the transformation of the neighborhoods with increased Latino populations. Findings showed that two areas of transformation increased largely because of the rise of higher density rental housing while one area transformed because two new affordable subdivisions were created within the studied time period. One new neighborhood's physical domain changed from an undeveloped land to a neighborhood with tract style houses. The historical areas have transformed in different ways including a decrease in crime and an increase in the younger population. The historical areas have experienced little change in the physical domain. All neighborhoods studied had evidences of a Spanish speaking population, and have businesses that cater to the surrounding Hispanic population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".