How research funding shapes academic outputs: Evidence from communication research paper characteristics and thematic trends in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.167 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.069 | 0.114 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.047 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 it