Country governance, tourism and environment quality: An emerging economy perspective
Bibliographic record
Abstract
The objective of the study has two folds: first, the study analyzes the role of country governance in tourism. Second, the study investigates the impact of tourism on the environmental quality. For this purpose, the data from 1997 to 2018 are collected from the World Bank and Global Economy. Augmented Dickey Fuller (ADF) test and ordinary least square regression models are used to analyze the impact. The study finds positive impact of rule of law, control of corruption, political stability and government effectiveness on tourism. The study also finds a negative relation between tourism and environmental quality of Pakistan. The study recommends the tourism destination planners to continuously monitor their country and to work with the government towards stability and protection and safety of tourists and for the general public. The rules to control corruption must also be implemented in a blanket form. The study also recommends the researchers to promote research on the relationship between country governance and tourism. Overall, the evidence of the study provides innovative information regarding the impact of country governance on tourism and tourism on environmental quality, which political leaders, tourist analysts and policymakers can use to shape policies in order to promote the tourist industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".