Preference of Spatial Mobility and Reside in the Suburbs Indonesia
Bibliographic record
Abstract
The desire to get a natural life, away from pollution, and comfortable with guaranteed utilities and facilities is why people choose housing in the city's hinterland. This study aims to analyze the determinants of spatial mobility preferences and to live in the city's hinterland. The research method used was a survey with 400 respondents from Rimbo Panjang and Karya Indah village in Kampar City. They have carried out spatial mobility and resided for the last five years. Data was collected through questionnaires and direct interviews, which were analyzed using a Likert scale questionnaire. Furthermore, SEM analysis was carried out using AMOS and SPSS software. The study results showed that three dependent and one intervening variable significantly influence the preferences of spatial mobility and living variables with a probability value below 5%. Only the residential environment variable (x1) with a score of 0.050 means it is not significant with a probability value (5%). Adjusted R Square value of 0.625 shows that the variation of the independent variables affects variations in spatial mobility preferences and resides by 62.5%. The frequency distribution of respondents' answers shows that all independent and dependent variables are in a good category. The research findings will be more diverse with the use of other methods and samples that represent the social mobility preferences of people in Indonesia. This study begins with a wide range of topics from the city center to the suburbs. The limitation of this study is that it does not distinguish whether they are from Pekanbaru City or other areas. Future research that could utilize longitudinal data could pursue a more significant measure of the relationship between regions in the study of mobility and settlement preferences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".