How research funding shapes academic outputs: Evidence from communication research paper characteristics and thematic trends in China
Bibliographic record
Abstract
ABSTRACT Purpose To explore how different types of research funding affect research papers, with implications for optimizing funding policies and promoting sustainable research development. Design/methodology/approach We used social network analysis and citation analysis to compare the influence of funded and non-funded papers, as well as among different funding types. Multidimensional scaling and cohesive subgroup analysis revealed thematic differences. Findings Funded papers do not always show higher academic influence than non-funded ones, but multifunded papers perform better than single-funded ones. Papers funded by international institutions and HKMT have a greater impact on the international academic community. Funded papers emphasize innovation and interdisciplinarity; non-funded papers focus more on classical theory application. Research limitations This study used only the WoS Core Collection, potentially missing other funding sources. Practical implications The findings inform the refinement of funding policies and support strategies that encourage impactful and innovative research. Originality/value This study offers a multi-level empirical analysis of how funding shapes research influence and thematic trends.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".