Bibliographic record
Abstract
Even though, in the past, competition depended on the factors of production possessed, today it depends on the production of value-added goods, their export, and finally, branding of the country today. Since the late 1990s, the brand value of countries has been an important concept that has been studied. Current academic literature is deprived of weighting sub dimensions of country brand strength index and compare index values by years. Having an import-ant role in academic literature, Fetscherin (2010) identified five dimensions of the country brand strength index as export, tourism, foreign direct investment, migration and governance, but not giving any weighting to sub dimensions. In order to contribute to current country brand index literature, sub-dimensions of the index are weighted with the help of the analytical hierarchy process (AHP) method, comparing 2010 and 2015. Therefore, the innovation of this paper is its weighting method and the comparison of index values by years. The Country Brand Strength Index (CBSI) is calculated for G7 countries and Turkey using the survey based AHP method, consisting of 5 different indi-cators: exports, foreign direct investments, tourism, immigration, and governance. According to the results, it is deter-mined that “exports” has the most important weight among those indicators with Canada leading the group with the best index value in 2010 and 2015. The aim of this study, which was conducted with limited resources, is to shed light on studies to be carried out in the future in order to establish a strong country brand and increase country competi-tiveness in the international markets. In this respect, repetition of this research as regards to geographical and regional variations and performing qualitative and quantitative studies, incorporating different dimensions in the index such as culture, science and technology, will strengthen the academic literature in this field.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".