Analyzing and investigating the indices and factors of creative tourism in urban rusty contexture of Isfahan (Case study: Joybere quarter)
Bibliographic record
Abstract
Purpose: the main purpose is analyzing the indices and factors of creative tourism in rusty contexture of Joybare in Isfahan. Method: the method of the study is descriptive analytic and the tool is a researcher-made questionnaire with 55 general and special questions. The statistic population was inhabitants of Joybare in Isfahan about 11363 people. The sample size was calculated 156 people by Sample Power software. Validity was face validity and reliability was 0.923 by Alpha Cronbach’s coefficient. Analytic and inferential analyses have been performed by SPSS, one sample T-test, ANOVA, LSD, Duncan, and friedman tests. Findings: The results of t-test shows that status of factors of cooperation, service and welfare facilities, reconstruction and mending, cultural attractions and historical monuments have been less than the mean average of the test. Informational factors, advertising, creativity, and value creation have been in half average status in tourism. Results: the results of ANOVA test with significant level of 0.009 demonstrate inequality of the average of the status of factors of development of creative tourism. For confirmation of the difference of the effect of the investigated factors; creativity and value creation in tourism along with notifying and advertising have been put in a separate group. In the results of the rating the status of the indices of creative tourism by friedman test, the highest level of average at first step is related to the variables of creativity and value creation and next to reconstruction and mending.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".