Bibliographic record
Abstract
Iran and especially Lorestan province by having historical, cultural and natural attractions which have special tourism privileges on both national and global levels, yet are unable to acquire a share of tourism.This study has been conducted to examine tourism policies on province and national levels in order to develop the tourism industry in Lorestan province.In this regard, some questions are raised, like: 1.How the damages of tourism affect social_economic development in Lorestan?How the management, policy_maker bodies, advertisments and informing entities of Lorestan province play roles in tourism industry development?To respond these questions, it was found that: Weak performance of tourism authorities, Lack of infrastructure development, Lack of trained and skilled manpower, Lack of awareness and advertising, a negligible specialized public funds, Lack of private investors attractions, Lack of knowledge management rather than policy-driven management are introduced as tourism damages in Lorestan province.This research uses descriptive-surveying method in terms of data collection and in terms of purpose, it is applied.In order to collect data and to analyze information, the library studies, interviews and questionnaires have been used .At the end, some recommendations have been presented on tourism development in Lorestan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.817 | 0.715 |
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".