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Record W4286669373 · doi:10.1111/padm.12878

The effect of institutional support and relational capital on knowledge mobilization in public administration research

2022· article· en· W4286669373 on OpenAlex
Wenguang Zhang, Yanbo Xiao, Jingyu Zhang, Ji Lu

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublic Administration · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInstitutionRelational capitalMobilizationKnowledge managementDisseminationChinaPublic relationsHuman capitalAdministration (probate law)Capital (architecture)BusinessPolitical scienceSociologyComputer scienceSocial scienceEconomicsEconomic growthIntellectual capitalGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.376
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it