Toward an Agenda of High-Priority Tourism Research
Bibliographic record
Abstract
The Travel and Tourism Research Association (TTRA) creates networking opportunities to share perspectives on research issues related to the planning and marketing of travel and tourism. While its publications offer numerous suggestions for case- and context-specific research, rarely have attempts been made to develop a membership-wide agenda of priority topics. In an age of increasing resource scarcity and calls for accountability, the need for such an agenda is growing. This study prioritizes management- and method-related research topics deemed by TTRA members to be critical to travel and tourism decision makers over the next decade. It describes the member-based process used to establish these priorities, and suggests specific directions for investigations associated with each of them. It calls for greater dialogue around the relative priority placed on these topics by practitioners and academics within TTRA, as well as extending the discussion to other influential industry and professional travel and tourism organizations.
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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 | 0.308 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.026 | 0.037 |
| Scholarly communication | 0.063 | 0.050 |
| Open science | 0.008 | 0.042 |
| Research integrity | 0.027 | 0.047 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".