Impact of Spatial Configuration on Promoting Lifelong Learning Development in Pathum Thani, Thailand
Bibliographic record
Abstract
A “Learning City” uses its resources to enhance learning opportunities for individuals and communities that promote social cohesion, cultural prosperity and economic development. While the UNESCO network of learning cities provides guidelines for measuring social and economic prosperity, there have been no studies examining the current strengths and weaknesses of such cities in Thailand. The purpose of this study was to identify current strengths and opportunities for improvement in the Thanyaburi district of Thailand. We surveyed 400 residents to examine formal and informal learning activities, followed by layering the survey data using geographic information systems, to determine geographic differences in population size, density and transportation access. The findings show that formal and informal learning activities differ by the density and diversity of various geographical locations within the district according to urban centrality scores. The most popular activities were community-based, environmental and educational activities, respectively. However, various municipalities had few learning opportunities for local residents. Promoting lifelong learning opportunities is an essential response to establishing a vibrant environment for individuals, communities and cities and is a key driver to improving economic development (e.g., employment and education) and sustainability.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".