Research on the Cooperative Behavior of Academic Papers Published by Chinese Educational Scholars Based on Complex Networks
Bibliographic record
Abstract
Research on mutual cooperation among scholars or research institutions has become more and more common. Thepurpose of this paper is to explore the current status of cooperation between scholars and research institutions in thefield of Chinese education. In this paper, we use the method of the complex network to analyze the cooperativebehavior of academic papers published by Chinese educational scholars by collecting academic papers on educationleadership, education policy, quality education, and vocational education. Our conclusions show that most of theacademic papers published by Chinese educational scholars are non-cooperative. In the authors of the co-authoredpapers, there is a significant "Matthew effect", that is, some key scholars in these fields that link the collaborators.Lastly, there is no obvious aggregation effect between the authors of the co-authored papers which indicating awidespread and extensive connection between the collaborators. The above conclusions provide valuable insightsinto our understanding of the cooperative behavior of Chinese education scholars.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".