An Analytic Hierarchy Process (AHP) Approach to Identifying Key Criteria of Taiwan’s National Brand
Bibliographic record
Abstract
Nation branding benefits industrial upgrading within a nation and closes the competitiveness gap between nations. A nation that lacks a strong, positive, reputable national brand cannot maintain its competitiveness aimed at attracting consumers, tourists, investors, or immigrants and cannot gain the respect and attention of other nations or the global media. All nations are currently vying to create their own national brands. Taiwan has also attempted to define its advantages and develop its national brand to not only respond to current development trends, but also to examine issues facing Taiwan’s development. Because of this, key criteria of Taiwan’s nation branding were identified in this study. The expert interview methodology was used to discuss and compile a criteria system of Taiwan’s national brand and the analytic hierarchy process technique was used to calculate the relative weights for these criteria. Results showed that the most suitable criteria for Taiwan’s nation branding were based on the dimension of culture; within this dimension, the criterion historical heritage was most crucial. This study can serve as a reference for the government when it needs to determine areas to focus on in nation branding.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".