Perceptions on Urbanization Impact on the Hinterlands: A Study of Khon Kaen City, Thailand
Bibliographic record
Abstract
This research article aims to study the impact of urbanization on its hinterlands, by focusing on the perceptions of the household heads in the hinterlands of a small city, which is the major type of the cities in the Mekong region. Khon Kaen city in the northeast region of Thailand was selected as a case study. A questionnaire survey was conducted with a sample of 409 households in 35 villages in the city’s hinterlands, defined as the areas of 5 kilometres lying further from a ring road surrounding the city. Data were analysed by descriptive statistics and mean ranking. The research found that the top ten most favorable impacts were on better access to health and educational services, better employment and educational opportunities, women’s decision making power and social status, as well as positive impact on local government. As for the negative impacts of urbanization, pre-marriage sexual relationship, co-habitation and inappropriate dressing of village youths were identified as major problems. Apart from youth’s problems, other social problems namely gambling and drug addiction were identified, as well as household economic problems including shortage of farm lands, higher household expenditure and debts. Among the top ten highest ranks of negative impacts, only one environmental problem, namely noise pollution, was identified. However, it is remarkable that environmental sector contained the highest numbers of unfavorable impacts that had mean value less than three. In other words, among the seven sectors explored, namely the household economy, the village economy, the society, the political and administration, the ideology and culture, the women and youths and the environment, the research found that environmental sector got the highest number of negative items of urbanization impacts. Based on a Multiple Regression Analysis, the research found that household heads’ age (Beta = 0.127) and monthly income (Beta = -0.175) were the factors that influenced their perceptions on the impacts with the R2 = 0.073.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".