Recent Personnel Reforms of Public Universities in China and in Italy: A Comparison
Bibliographic record
Abstract
Purpose of the present research is an investigation of the most recent personnel reforms of higher education institutions in China and in Italy. A one-to-one comparison between the two realities would have been unrealistic, given the enormous differences between the two Countries in size and historical development. We focused our analysis on some basic issues common to both higher education institutions, such as the degree of the academic autonomy from the political power in the academic governance and the quality of the knowledge production and transfer to the society. The Sun Yat-Sen and the Guangzhou Universities in the Guangdong Province of China, and the Universities of Padua and Ca’ Foscari in Venetian Region of Italy, have been chosen as case studies.In China the personnel reforms introduced by the central government in the period 1995-2014, were accompanied by a relevant financial support by the central and regional authorities, thus helping the national universities to attain high standards of excellence in the technological domain. Those remarkable financial investments by the central and regional authorities are paying off, contributing to the technological advancement of the Country.As for the Italian public universities, a very innovative reform law was introduced by the ‘Ministry of Education, University and Research’ in December 2010, which granted a high level of governance autonomy to those institutions. Unfortunately, the great financial crisis that hit the Country in the same period of time caused a strong reduction of the public funds to universities and a consequent brain drain of young post graduates toward Northern Europe and North America.In spite of this temporary shortage of funds, Italian public universities have maintained their high level of excellence in science, technology and humanities, as evidenced by the increasing number of their bilateral cooperation agreements, concerning student mobility and joint research activities, with foreign universities all over the world, China included.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".