A fuzzy application on MICE hosting: An Iranian case study for locating suitable areas based on P.L Indexes
Bibliographic record
Abstract
Today, countries compete to gain political, economic, social and cultural advantages.In addition, cities are trying to demonstrate their predominance on local and regional levels and achieve development by using managerial science.Meetings, incentives, conventions and exhibitions tourism (MICE) hosting as a significant element of urban tourism is one of the effective methods to obtain urban development around the world.However, no specialized planning and independent investments in regard to these activities have been organized in Iran.Aiming to identify suitable areas in the northern part of Iran, the present research intends to recognize the necessary physical and location related (P.L) indexes to perform the specialized activities of MICE tourism in this region.So at first, indispensable indicators to accept special role of MICE tourism are identified and then using GIS and fuzzy method, adapted areas are assigned.The result map shows that a ribbon-like area near the Caspian Sea with two wide lands at central and western parts is a proper choice to host MICE activities in the region from the viewpoint of P.L indexes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".