A Multi‐Criteria Classification Approach for Identifying Favourable Climates for Tourism
Bibliographic record
Abstract
ABSTRACT The aim of this paper is to present a multi‐criteria classification approach for identifying world climates that are favourable to light tourism. We use a multi‐criteria aggregation method, Electre Tri‐nC, to assign over 60 000 world locations to one of four climate categories ranging from unfavourable to ideal. The motivations behind this work are to remedy to some of the methodological problems in composite indices such as the Tourism Climatic Index, where a weighted sum is computed using ordinal data. We present our results for the summer month of August on the basis of the years 1961–1990 derived from the CRU CL 1.0 climate database of New et al. (1999). In addition to being theoretically sound, our approach uses the original, virtually untransformed, continuous data thereby avoiding loss of information. It also minimizes the compensation effects and makes it possible to take into account additional criteria to cater to various tourism contexts with various decision maker profiles. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".