ÜLKELERİN REKABET ÜSTÜNLÜĞÜ GELİŞTİRMESİ: MARKA GÜÇ ENDEKSİ ÇALIŞMASI
Bibliographic record
Abstract
Today, countries can compete in the international markets by virtue of their strong brands. This competition occurs in export, foreign direct investment, attracting qualified human resource and increasing tourism receipts. The competition was over the factors of production possessed in the past, whereas it depends on the production of value added goods, their export and finally branding of the country today. Since the late 1990s, brand value of countries has been an important concept that started being studied. Academic literature, measuring the country brand value is limited. In this manuscript, Country Brand Strength Index (CBSI) is calculated for G7 countries and Turkey using survey based Analytical Hierarchy Process (AHP) method having 5 different indicators that are exporting, foreign direct investments, tourism, immigration and governance. According to AHP method, it is determined that “export” has the most important weight among those indicators and Canada is ranked as the country that has got the best index value. It is aimed that the index results will shed light on the studies to be carried out to establish a strong country brand and increase their competitiveness in the international markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".