The Mean Difference of Religiosity between Residents of Rural Areas and Urban Areas of Mahmoudabad City
Bibliographic record
Abstract
The main objective of this research is to study the mean difference of religiosity between the residents of rural areas and urban areas of Mahmoudabad City. For the measure of religiosity, Glock and Stark's (1965) model of religiosity is used. For the analysis of the theoretical perspectives, theories of IbnKhaldun, Tonnies, Durkheim, Giddens and Martin are used. The statistical population of the research consists of samples are 400 people. Half of the sample is from rural areas and the other half are from urban areas. This research is conducted based upon survey. The data obtained from the survey is described and analyzed by using SPSS software. The statistical methods are demonstrated and analyzed in two parts of descriptive statistics and inferential statistics. The findings of the research show that there is a significant difference in belief and ritual dimensions of religiosity between rural residents and urban residents. The levels of belief and ritual dimensions of religiosity are higher in rural residents in comparison with urban residents. In addition, among sub-dimensions of ritual religiosity, only intellectual religiosity has no significant difference between rural and urban residents. Moreover, the level of religiosity of total urban residents and rural residents has a significant difference and the level of religiosity in rural residents is higher than urban residents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".