Optimising public transport for reducing employment barriers and fighting poverty
Bibliographic record
Abstract
Alleviating poverty in low-income and developing nations is integral to social stability, attracting investments and generating employment opportunities which in turn elevate people's well-being.Employment could be encouraged through a combination of direct (travel time and training) and indirect measures (social institutions like childcare and elder care).Other factors such as legal reform and infrastructure services could help as well.Transit is perhaps the first element (but not the only one) required to provide better access to the labor market, to health and educational facilities and to social institutions.Transit should be cheap, fast, safe, and secure to reach to most travelers within the influence area.The main objective of this research is to propose a method to fight poverty through better access to employment by a proactive cost-effective planning of investments in existing and future public transit systems.A decision-making system is developed to assess the current employment situation in different geographical regions considering unemployment rate, access to jobs and public transportation systems.Real data from a case study of the Costa Rica metropolitan area is used to illustrate the applicability of the proposed approach.The results show that the proposed model can lead governments to a cost-effective solution that decreases the employment barrier index by more than 50% during the first 5 years.The proposed model will be beneficial for transit agencies in charge of BRT, Tramway, and suburban trains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".