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Record W3034873775 · doi:10.22230/ijepl.2020v16n9a967

A developmental evaluation of research-practice-partnerships and their impacts

2020· article· en· W3034873775 on OpenAlexaffvenue
Amanda Cooper, Samantha Shewchuk, Stephen MacGregor

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsQueen's University
Fundersnot available
KeywordsAdaptabilityEquity (law)Knowledge managementGeneral partnershipProcess managementBest practiceSustainabilityBusinessPublic relationsPolitical scienceComputer scienceManagement

Abstract

fetched live from OpenAlex

Globally, K-12 education systems are grappling with how best to integrate research and evidence into policy and practice. Research-practice-partnerships (RPPs) have arisen as a potentially powerful mechanism for school improvement. This study investigates the ways four research-practice-policy-partnerships are addressing impact by (a) reporting on metrics being used to assess brokering and partnerships, and (b) exploring the ways that network leads and policymakers conceptualize partnerships and impact on the frontlines. Our findings suggest that while metrics being used provide a necessary baseline for number and types of partnerships, more robust methods are needed capture the quality of interactions and to strategically inform network development. Network leads conceptualize impact in relation to collaborative processes (shared goals; new and diverse partnerships; improved student achievement; system alignment); systems and structures (joint-work; funding and sustainability; demand from practitioners; equity); continuous learning (capacity-building; reach; adaptability; storytelling). Our discussion provides ideas about network improvement that include sharing cases of failures (alongside exemplary cases) to maximize learning, and advocates for the use of developmental evaluation to explore the impacts of RPPs.

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)0.008
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.815
GPT teacher head0.599
Teacher spread0.217 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations16
Published2020
Admission routes2
Has abstractyes

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