A Study of Impact of Tourism Direct Employment Trends on Tourism Arrivals: An Empirical Analysis of Sri Lankan Context
Bibliographic record
Abstract
Sri Lanka is one of the major tourist attraction destinations in South Asian region. After the economic reforms in 1977, the successive governments implemented various attractive policies and programmes to promote tourism in pursuing economic growth and development. The government further employed a number of initiatives to encourage and attract tourism arrival in the country. In this backdrop, this study is to analyze the impacts of the tourism direct employment trend on tourism arrivals in Sri Lankan context by using the time series data from year 1978 to 2017. The dependent variable used in this study is tourism arrivals. The independent variables are tourism direct employment and tourism Earnings. The tools used to achieve the objective of this study are Correlation Analysis, Multiple Regression, and Residual test. In this study, it is found that the correlation relationship between the variables is very strong. tourism direct employment and Tourism Earnings are directly related with tourists’ arrivals. The data collected have analyzed by using the econometrics software EViews 10. Based on the result recommended that to increase the magnitude of the direction tourism arrival in Sri Lanka, the factors such tourism direct employment and tourism Earnings should play statistically significant roles. This significant role should be considered by the relent authorities in Sri Lanka.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".