Pathological Evaluation of Using Scenario Planning in Academic Theses Focusing on Tourism-Related Subjects
Bibliographic record
Abstract
Given the increasing number of studies using the scenario planning (SP) approach, particularly in the field of tourism, the aim of the present study is to evaluate the challenges and deficiencies in theses that have employed Futures Studies (FS) using the SP method in the context of tourism, and then categorize these challenges into groups that describe them. Twenty-three in-depth interviews were carried out with university graduates, as well as university professors. Data analysis was performed using MAXQDA software. The results show the most important challenges of using this method include 1) inadequate knowledge of the students about FS research methods, 2) not knowing the nature of uncertainties, especially in the tourism context, 3) research topics being inconsistent with the needs of the Iranian tourism sector, and 4) the lack of facilities to share FS knowledge between the universities that are active in tourism area in Iran. Recommendations are provided to address these challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".