From rejection to acceptance: the institutionalization of adopting university ranking outcomes as policy and strategic tools in China since the 1980s
Bibliographic record
Abstract
China is an important player in global university ranking exercise. Nevertheless, existing studies have not adequately explored how the legitimacy of adopting university ranking outcomes has been chronologically established on Chinese soil. This paper attempts to fill this knowledge gap drawing on interviews with 37 stakeholders between 2003-2021 and an analysis of 2086 articles and reports published between 1984–2022 concerning university rankings. It first analyses the process of institutionalization of adopting university ranking outcomes in China, and discusses how the policy initiatives such as the ‘985 Project' in 1998 and the ‘Double First-Class' Project in 2016 intertwined with university rankings over the time and provide opportunities for establishing such legitimacy. Secondly, it analyses the mechanisms through which university ranking outcomes gain legitimacy, and suggests that interactions among the stakeholders are one of the key mechanisms, whereby the central government plays a pivotal role in legitimizing adoption of international university ranking results. Furthermore, we argue that the universities have responded actively to adopting the ranking outcomes and use them as strategic tools to achieve their own goals. As such, this paper sheds a new light on the impact of university rankings in China and beyond.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".