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Record W3112646295 · doi:10.1108/jpcc-11-2019-0031

A research model to study research-practice partnerships in education

2020· article· en· W3112646295 on OpenAlexaff
Amanda Cooper, Stephen MacGregor, Samantha Shewchuk

Bibliographic record

VenueJournal of Professional Capital and Community · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsQueen's University
Fundersnot available
KeywordsCoproductionProcess (computing)Knowledge managementComputer scienceAdaptation (eye)General partnershipProcess managementTest (biology)Field (mathematics)Corporate governanceData scienceManagement sciencePolitical sciencePublic relationsPsychologyBusinessEngineering

Abstract

fetched live from OpenAlex

Purpose This scoping review utilizes findings from 80 articles to build a research model to study research-practice-policy networks in K-12 education systems. The purpose of this study was to generate a broad understanding of the variation in conceptualizations of research-practice-policy partnerships, rather than dominant conceptualizations. Design/methodology/approach Arskey and O'Malley's (2005) five stage scoping review process was utilized including: (1) a consultative process with partners to identify research questions, (2) identify relevant studies, (3) study selection based on double-blind peer review, (4) charting the data and (5) collating, summarizing and reporting the results in a research model identifying key dimensions and components of research-practice partnerships (RPPs). Findings Coburn et al. (2013) definition of RPPs arose as an anchoring definition within the emerging field. This article proposes a model for understanding the organization and work of RPPs arising from the review. At the core lies shared goals, coproduction and multistakeholder collaboration organized around three dimensions: (1) Systems and structures: funding, governance, strategic roles, policy environment, system alignment; (2) Collaborative processes: improvement planning and data use, communication, trusting relationships, brokering activities, capacity building; (3) Continuous Learning Cycles: social innovation, implementation, evaluation and adaptation. Research limitations/implications By using a common framework, data across RPPs and from different studies can be compared. Research foci might test links between elements such as capacity building and impacts, or test links between systems and structures and how those elements influence collaborative processes and the impact of the RPPs. Research could test the generalizability of the framework across contexts. Through the application and use of the research model, various elements might be refuted, confirmed or refined. More work is needed to use this framework to study RPPs, and to develop accompanying data collection methods and instruments for each dimension and element. Practical implications The practical applications of the framework are to be used by RPPs as a learning framework for strategic planning, iterative learning cycles and evaluation. Many of the elements of the framework could be used to check-in with partners on how things are going – such as exploring how communication is working and whether these structures move beyond merely updates and reporting toward joint problem-solving. The framework could also be used prior to setting up an RPP as an organizing approach to making decisions about how that RPP might best operate. Originality/value Despite increased attention on multistakeholder networks in education, the conceptual understanding is still limited. This article analyzed theoretical and empirical work to build a systematic model to study RPPs in education. This research model can be used to: identify RPP configurations, analyze the impact of RPPs, and to compare similarities and differences across configurations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.093
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.907
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.076
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0220.019
Science and technology studies0.0070.023
Scholarly communication0.0190.034
Open science0.0060.010
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0110.003

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 head0.760
GPT teacher head0.637
Teacher spread0.122 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations40
Published2020
Admission routes1
Has abstractyes

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