Different measures of international faculty and their impacts on global rankings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study analyzed how the ranking status has changed at various higher education system levels by applying different definitions of international faculty. Among the four measures (birthplace, current citizenship, and the country of bachelor and doctoral education), this study found that international faculty measured by the country of doctoral studies produced significantly different international outlook scores and thus ranking status from that based on birthplace or citizenship. Specifically, major English-speaking systems such as the UK, Canada, and Australia hire a large number of faculty who are foreign citizens while non-English speaking systems (Italy, Portugal, China, Korea, and Brazil) hire more local academics who have earned their doctoral degree abroad. This suggests that these non-English speaking countries are systematically under-rated in their international outlook scores by the adoption of the birthplace-based or citizenship-based international faculty measures. As an alternative, this study proposes to update the international faculty measure using a combination of citizenship of employment and doctoral training to minimize this systemic bias.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it