A framework for an industry supported destination marketing information system
Bibliographic record
Abstract
This article provides guidelines for the establishment of a comprehensive state/provincial destination marketing information system (DMIS). More specifically, it describes the process by which the tourism industry in Alberta, Canada developed a framework for the acquisition of timely research and intelligence to maintain and enhance its competitiveness as a travel destination. In keeping with a government mandate for greater public–private sector partnership, consideration was given to two equally important, but functionally distinct end users: (1) Travel Alberta, the province’s destination marketing organization, which uses information to guide strategic marketing priorities and create cooperative marketing opportunities for industry, and (2) local tourism operators, who demand information to improve their marketing decisions in service of individual business objectives. The needs of these key audience groups were identified and addressed using a comprehensive three-step approach: (1) Interviews were conducted with key industry players to identify research and intelligence needs, (2) information sources were sought to respond to the identified needs, and (3) solutions were identified to deliver high-quality information at an affordable cost. The end result of this process is a framework that can serve as a useful model for other jurisdictions seeking to develop a DMIS. While the Alberta framework will require ongoing evaluation to ensure its validity and accuracy, it possesses an important quality frequently lacking in this type of
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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 | 0.031 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".