Measuring efficiency in tourism: A problem of shared factors and multiple attributes in DEA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current research makes three main contributions to the DEA (Data Envelopment Analysis) literature. First, when using DEA to derive an efficiency score for a given DMU , it is normally assumed that each and every DMU has its own unique set of inputs and outputs, there are situations whereby a DMU can have a factor that is shared with other DMUs. This means that one must view efficiency from the perspective of groups of DMUs rather than from the perspective of the individual DMU. Second, two stage problems can, in the presence of shared factors, result in different groupings of DMUs in one stage than in another . Third, in certain circumstances efficiency can be viewed from the perspective of multiple attributes (e.g. different types of tourism). Herein, we develop a model to cater for these features and illustrate the model using a data set on tourism in Mexico .
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it