Bibliographic record
Abstract
Considering the fact that today there is a significant increase in using public communication devices and social network, the communication and interaction among people in a community is decreasing and concept culture transfer is being done slowly.Meantime tourism as a factor of creating and improving resorts is a suitable context.The resorts in a suitable context that possesses the potential needed to reach this goal would create communication and interaction among tourist and citizens of the host community and apart from creating occupational, financial and other opportunities it would have a chance for culture and social interactions.The goal of this research is to create a suitable context in order to reach the intended goals.So that would be appropriate to study such context in Rasht and in Guilan province which have always possessed a high amount of tourism potential and culture .The present research, in order to answer the question of the study, has considered the effective factors on interaction between tourist and citizen in architecture .The method of research was analytic-descriptive that after preparing questionnaires and distributing them among 160 tourists and citizens was analyzed by SPSS and Fridman and deductive tests.It showed that all the hypotheses are accepted and by ranking independent variables existence of public spaces in structure was chosen by the responders as the most important factor, and after that creating local and traditional context and architecture, the area of cultural works of the region, developing the structure in the site, and availability of roads to other cities, were the next factors.Hence, it can be concluded that designing according to the variables which have been studied can be a proper element to improve social interaction among tourist and citizens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.899 | 0.911 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".