Bibliographic record
Abstract
Petra is a mature tourism destination in the south of Jordan with a degree of seasonality over the last 10 years. Despite the recognized importance of Petra for the tourism industry in Jordan, there has been a lack of studies that discuss seasonal demand variations and its impacts on other related industries in the region. Yet, this study aims at analyzing patterns and effects of seasonality of tourism demand in Petra for the period 2006-2017. The required data was obtained from Ministry of Tourism and Antiquities (MoTA). Four methods were used to measure tourism seasonality in Petra. These are: Seasonality indicator; Seasonality ratio; Gini coefficient; and Seasonality index. The results of the study showed a modest level of tourism seasonality in the study area. Among methods, Seasonality index appeared to be the appropriate and simple way to calculate seasonality patterns at tourism destinations. The results showed that there are two peaks of seasonality in tourism demand of Petra. The first and the highest one was in April and the second took place in the months of October and November. In addition, seven months represented the low season of tourism demand in Petra. These are December, January and February as well as June, July, August and September. The tourism seasonality in Petra based on that is mainly due to the weather in these months which represent the coldest and warmest months in the year respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".