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Record W4402328208 · doi:10.1101/2024.09.06.24313221

Knowledge mobilization with and for equity-deserving communities invested in research: A scoping review protocol

2024· review· en· W4402328208 on OpenAlex

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

VenuemedRxiv · 2024
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMobilizationProtocol (science)Equity (law)BusinessCommunity mobilizationKnowledge managementPublic economicsPolitical scienceEconomicsComputer scienceEconomic growthMedicine

Abstract

fetched live from OpenAlex

Abstract The practice of putting research into action is known by various names, depending on disciplinary norms. Knowledge mobilization, translation, and transfer (collectively referred to as K*) are three common terminologies used in research literature. Knowledge-to-action opportunities and gaps in academic research often remain obscure to non-academic researchers in communities, policy and decision makers, and practitioners who could benefit from up-to-date information on health and wellbeing. Academic research training, funding, and performance metrics rarely prioritize or address non-academic community needs from research. We propose to conduct a scoping review on reported K* in community-driven research contexts, examining the governance, processes, methods, and benefits of K*, and mapping who, what, where, and when K* terminology is used. This protocol paper outlines our approach to gathering, screening, analyzing, and reporting on available published literature from four databases.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptMetaresearchScholarly communication
Domain: Methods · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
grokMetaresearchScholarly communicationOpen science
Domain: Methods · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
opusMetaresearchScholarly communication
Domain: Reporting · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.023
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.492
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.972
GPT teacher head0.829
Teacher spread0.143 · 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