Measuring institutions’ country brand authenticity in transnational higher education
Bibliographic record
Abstract
When higher education students choose to enrol at a transnational education (TNE) or country-branded institution, they may want, and probably expect, an authentic ‘foreign’ educational experience. Most of the existing authenticity frameworks are not concerned with the contributory factors, and they are generic rather than industry or sector-specific. Hence, the aim of this research is to develop a measurement scale for country brand authenticity, specifically for use in transnational higher education settings. Researchers can use the scale to further explore antecedents and consequences of country brand authenticity. Also, a measurement scale for country brand authenticity enables institutions to map their existing performance against the key indicators, set clear targets and assess improvements in authenticity performance. The research built upon a conceptual framework adopted from the literature, and involved four phases of primary data collection: one with senior management expert informants and three with undergraduate students studying at American, British and Canadian affiliated universities in Bangladesh and the United Arab Emirates. The data for the research were collected using a deductive qualitative written questionnaire, one focus group, and two online quantitative survey questionnaires. The research results in a robust 29-item measurement scale for country brand authenticity in TNE.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".