Bibliographic record
Abstract
Purpose A qualitative development is discernible in China’s pursuit of global influence in knowledge following the launch of the Belt and Road Initiative (BRI). China has embarked on expanding the frontiers of its higher education and research enterprise in different geographies, a subset of its global power project. This paper employs the geointellect concept to analyze this phenomenon. Design/methodology/approach The paper applies the geointellect model, formed inductively, to illuminate China’s geographical expanse in higher education and research. Findings The BRI has provided a platform for China to shape the educational architecture of the participating countries, apart from receiving a boost in its prestige by leading educational alliances and opening overseas research centres. In quantitative terms, it has made progress in specific knowledge metrics. Nevertheless, certain challenges and limitations need to be overcome. Research limitations/implications The role of a foreign policy in boosting a country’s knowledge profile has been identified. Future research directions have been provided in using the geointellect model. Practical implications This study provides a direction to evaluate the implications of China’s foreign policy for its knowledge segment, especially in terms of capturing its leading prowess in higher education and research. Originality/value It contributes a conceptual model to capture the different facets of China’s geointellect, with foreign policy, geography, higher education, and research being its constituents.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".