Bibliographic record
Abstract
Abstract Tourism to the Third World continues to grow and, correspondingly, the promotion of these regions is increasingly popular and important. Although some concerns have been voiced about the images contained in Third World tourism marketing, there has been no extensive examination of its content to date. Accordingly, in this study, the verbal and visual components of 115 brochures for 12 Third World countries are analysed. To facilitate a detailed content analysis of these brochures, a ‘4A’ approach is introduced, focusing on attractions (natural and man‐made), actors (hosts and tourists), actions and atmosphere. This comprehensive inventory reveals that Third World countries are clustered into three groups, namely Oriental, sea–sand and frontier. It is shown that by emphasising and stereotyping certain attractions, actors, actions and atmospheres, certain overarching tourism myths are created around Third World destinations. Copyright © 2002 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".