Promoting Country Image and Tourism in New or Underdeveloped Markets
Bibliographic record
Abstract
In this paper, the theoretical framework of the Stereotype Content Model (SCM) was applied in the context of a host country promoting its tourism to new or underdeveloped markets. The host country’s perceived Warmth, Competence and Status were each relevant to an understanding of the attitude toward the country itself and as a tourism destination. However, in both instances, the influence of perceived Status was found to be direct and not, as existing SCM theory suggests, via Competence. The findings support Status being a primary dimension of human perception alongside Warmth and Competence within SCM theory. Warmth evaluations also dominated the prediction of Tourism Attitude but not Country Attitude, again contrary to established thinking. The influence on Tourism Attitude of both Uncertainty Avoidance and Individualism/Collectivism were moderated by the perceived Warmth and Status of the host country. Thus, the SCM requires adaptation to the context of tourism in underdeveloped markets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".