Practicing What We Preach in Tourism Education and Research
Bibliographic record
Abstract
As teachers of both strategic and operational management in tourism/hospitality programs, we constantly stress the importance of research as a means of providing critical information for destination planning and development. At the same time, in our capacity as managers of academic programs, it is not uncommon for many of us to overlook the use of research in the management of these programs. While we normally undertake market assessments before establishing new programs, and regularly gather operational data on teaching performance, this paper argues that based on the existing literature, tourism education program managers generally fail to take sufficient advantage of several strategic research approaches that are heavily used in other management contexts. Having recognized their own shortcoming in this regard, the present authors have attempted to rectify it by formally integrating a selected number of widely used research approaches into the overall management of their programs. The results, we believe, demonstrate just how valuable the use of formal program research can be in setting innovative directions for program design and development, for enhancing student satisfaction with specific courses, and for evaluating the overall efficiency and effectiveness of well established programs. Part I of the present discussion examines the use and usefulness of one major research technique for tourism education program design and management. The technique in question is Strategic Visioning.
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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 | 0.079 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.091 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".