The effect of institutional support and relational capital on knowledge mobilization in public administration research
Bibliographic record
Abstract
Abstract Knowledge mobilization (KMb) takes a programmatic approach to empower and motivate scholars to connect research with policy‐making through disseminating research to knowledge users, acquiring information from practitioners, and responding to the acquired information. The present study aims to investigate the influence of institutional‐level factors on researchers' KMb activities. One hundred fifty‐five researchers in the field of public administration across China participated in an online survey study. The participants reported their KMb activities, perceived institutional support, and relational capital. The results demonstrate that both the strength of institutional support and relational capital are positively associated with researchers' KMb activities. Moreover, the effect of institutional support tends to be stronger when an institution has more relational capital. The study highlights that research institutions should take programmatic approaches to empower their researchers to be actively involved in the knowledge co‐production process and make a systematic effort at the institutional level to build a well‐developed collaborative network.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".