Evaluating tourism potential: a SWOT analysis of the Western Negev, Israel
Bibliographic record
Abstract
SWOT (strengths, weaknesses, opportunities, and threats) analysis is a widely used method of evaluation employed in the business and planning worlds, including tourism planning, but there is little documentation on SWOT analysis in the academic tourism or geography literature.In this study SWOT analysis was applied more systematically in these areas, and rules for using it are suggested.The objective of this research is to show how SWOT analysis can be made more attractive, useful and accurate in research.This paper examines the current status and the potential of ecotourism in the Western Negev, in Southern Israel.The evaluation was conducted at the national, regional, and local levels using SWOT analysis.Data was gathered through field observation, interviews with decisions makers, and questionnaires distributed to the local population between the years 2000-2006.The findings relate to both the use of the SWOT technique as a research method and an evaluation concerning the tourism potential of the Western Negev.The findings relate to both the use of the SWOT technique as a research method and an evaluation concerning the tourism potential of the Western Negev.A simple diagram of the components of an enhanced SWOT analysis framework was developed, presented and used.It is suggested that this framework has wide applicability.The tourism industry is only in its infancy in the Western Negev, and thus this analysis can assist local decision makers by estimating the potential benefits and threats to their development.It is hoped that both academics and practioners would use the recommendations offered in the article for future research and for future development of the area.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".