Evaluating performance of Ontario tourism regions using a two-stage network Data Envelopment Analysis approach
Bibliographic record
Abstract
Performance evaluation of tourism destinations is critical to destination competitiveness, success and ability to generate economic benefits for local populations. This paper proposes a two-stage model of tourism destination production process, which during the first stage uses available resources to generate visits to the destination and during the second stage converts the visits into financial results. The model is used to evaluate efficiency of tourism regions in Ontario, Canada, in 2016 and 2017. The efficiency scores are derived using a two-stage network Data Envelopment Analysis (DEA) approach. Findings show that the proposed approach allows to identify variability of efficiency scores across the two stages, analyze spatial distribution of scores and identify trends over time. Four distinct groups of tourism regions are identified with respect to their efficiency patterns. Study findings contribute to the conceptual literature on destination performance and can be used by practitioners to design performance evaluation systems for destinations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".