People Who Commute to Work in Puerto Rico: Demographic Characteristics of the Event
Bibliographic record
Abstract
The social and economic development occurred in Puerto Rico from the 50’s caused diverse changes in geographic human settlements. Between 1950 and 1970 economic factors significantly affected the distribution. Since 1990 the search for higher quality life and welfare caused a redistribution of population, in this union a little planning and mass transportation effectively produced an increase in the number of people that commuted to work on their owned private vehicle. The study was made to analyze the characteristics of the population that commutes to work in P.R. The source of information used was the American Community Survey of P.R. 2006-2010. The analysis was descriptive. The island has 1.1 million workers aged 16 or more, 88% travels to work by car, truck or van, 79.6% traveling alone. Only 3% reported using public transportation. A 3.9% said that they walked to work and a 0.2% travel by bicycle. There was a higher rate in women using carpool to get to work. Slightly over half of workers work outside their municipality of residence. To them it takes 30 minutes on average to get to work, to 13.4% takes over an hour. Nearly one quarter these workers leave their home at 6-7 a.m. A 7% said not having home vehicle. In Puerto Rico, there is an urgent need to develop mass transit system to be available for the worker. An effective transportation system will contribute to strong health and collective security of our workers and our people.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".