Social inclusivity through public transportation: a strategic approach to improve quality of life in developing countries
Bibliographic record
Abstract
Summary Welfare services such as healthcare and education are commonly recognised as determinants of physical quality of life indices during the early phases of development in the third world. In order to benefit from these services, it is important that the general public have the mobility means to access welfare provision centres. However, very low vehicle ownership under the conditions of low per‐capita incomes and large shares of population living in deep rural areas prevent the masses from accessing such services, thereby retarding the process of social development, which is reflected in the very poor physical quality of life indices in low‐income countries. Public transportation could offer a viable and affordable solution to this apparent ambivalence. It could permit mobility for poor masses in spite of low per‐capita vehicle ownership enabled by the national income levels. The present research demonstrates this strategic niche through an econometric examination of the evolution of the physical quality of life indices such as maternal mortality, infant mortality and literacy as against the healthcare, education and affordable mobility proxies in post‐independent Sri Lanka. The country, which was then referred to as Ceylon, is often cited as a rare example of achieving social inclusion and reduced marginalisation and thereby a high social welfare standing, in spite of relatively poor per‐capita income levels. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".