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Record W4406849966 · doi:10.3389/feduc.2025.1555200

Editorial: Networks and knowledge brokering: advancing foundations, inviting complexity

2025· editorial· en· W4406849966 on OpenAlexaff
Stephen MacGregor, Joelle Rodway, Elizabeth Farley‐Ripple

Bibliographic record

VenueFrontiers in Education · 2025
Typeeditorial
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsOntario Tech UniversityUniversity of Calgary
Fundersnot available
KeywordsComputer scienceData scienceKnowledge management

Abstract

fetched live from OpenAlex

Across this special issue, the contributing articles illuminate how knowledge brokerage and relational networks can be harnessed-and sometimes challenged-to strengthen evidenceinformed policy and practice in education. Their findings offer new insights into the interplay of theoretical concepts, methodological approaches, and ethical imperatives that shape this complex terrain. Several contributions highlight the distinctive roles and practices of knowledge brokers. For instance, Malin and Shewchuk (2024) emphasize that knowledge brokers are not merely neutral intermediaries; rather, they are "actors whose activities and decisions must be understood contextually-e.g., in relation to the communities that are being connected and to brokers' placement within systems" (p. 3). Similarly, Caduff et al. (2024) explore how brokers' relational ecosystems both broaden and constrain their ability to mobilize resources and facilitate innovation through the strong and weak social ties they cultivate.In pushing beyond conventional frameworks, some articles spotlight relational networks as sites of strategic innovation. Bohannon et al. (2024) demonstrate how boundary infrastructures, such as co-designed professional learning opportunities and flexible organizational routines, help rural districts adapt and learn in dynamic contexts. Turner et al. (2024) extend this line of thought by mapping social networks related to mental health supports in schools. Their analysis reveals how patterns of interaction and trust-building open or close pathways for critical knowledge flows.Equity and ethics also figure prominently. Malin and Shewchuk (2024) advocate for an equity-centered lens, urging brokers to foreground issues of representation, power, and justice in their work. This stance resonates with Friesen and Brown's (2024) exploration of teacherleaders' professional learning, where the growth of confidence and capabilities is tied closely to the careful, context-sensitive design of relational activities that honour diverse perspectives.Methodologically, these studies introduce varied research designs-ranging from social network analysis to in-depth qualitative case studies-that yield a rich understanding of how knowledge moves through and transforms educational ecosystems. Collectively, the articles underscore a need for more approaches that capture complexity rather than oversimplify.In terms of implications, the authors suggest that policymakers, leaders, and practitioners who aim to strengthen ties between research, policy, and practice must attend to the subtleties of relationships, resources, and values. Rather than a technical fix, advancing equitable and impactful knowledge brokerage requires sustained reflection, dialogue, and openness to contextspecific adaptations.In recent years, scholars and practitioners have recognized that addressing complex issues-ranging from mental health supports in schools to rural capacity-building-cannot be achieved by simplistic, top-down evidence dissemination alone. There is a renewed emphasis on building relational infrastructures that acknowledge the multi-level interplay of policies, practices, and diverse forms of expertise (MacKillop et al., 2020). The articles presented in this research topic both reinforce and deepen this perspective. By examining relational ecosystems, boundary infrastructures, and equity-centered approaches, they suggest that knowledge brokerage and relational networks are integral elements of educational change, not just beneficial add-ons. Their collective insights resonate with an emerging scholarship that views relational networks as essential to leveraging complexity and mobilizing knowledge in service of local and global educational aims (Penuel et al., 2020;Rodway et al., 2021).For policymakers and practitioners, these findings imply that designing more flexible, equity-aware systems is crucial. Rather than imposing standardized reforms, leaders might consider strategies such as co-designing professional learning that respects multiple knowledge systems and power differentials. Such approaches can help ensure that local expertise is not overshadowed by distant authorities-a point highlighted when Bohannon et al. (2024) found that "even the best-intentioned external partners must negotiate shared ownership with rural educators" (p. XX).For researchers, there is a fertile landscape for future inquiries. Comparative, crossdisciplinary work could elucidate how relational networks evolve in varying socio-political contexts. Longitudinal research might track the lasting impacts of network-based interventions, while other methods-such as critical ethnographies or participatory action research-could surface subtle power imbalances that shape learning processes over time. These studies prompt a renewed attentiveness to the human, relational dimension of educational change. The educational challenges faced worldwide call for approaches to change that value complexity and contextual nuance. By continuing to explore this terrain and by refining methodologies to capture the contours and dimensions of knowledge brokerage in relational networks, educational communities can move closer to realizing meaningful, sustained improvements that are both evidence-informed and locally resonant.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.046
GPT teacher head0.402
Teacher spread0.356 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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