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Record W1512802959 · doi:10.22230/src.2014v5n3a166

Strategies for Sustaining Complex Partnerships

2014· article· en· W1512802959 on OpenAlexafffundvenueabout
Naomi Nichols, Stephen Gaetz

Bibliographic record

VenueScholarly and Research Communication · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaYork University
KeywordsInteractivityGeneral partnershipReciprocity (cultural anthropology)StakeholderPublic relationsKnowledge managementKey (lock)Political scienceComputer scienceWorld Wide WebPsychologySocial psychology

Abstract

fetched live from OpenAlex

This article details the role that networks play in the creation and implementation of a comprehensive knowledge mobilization strategy. Using the activities of the Canadian Homelessness Research Network (CHRN) as a case study, the authors draw on in-depth interviews, participant observation, and document analysis to understand how the interactivity cultivated in a multi-stakeholder partnership can increase the impact of research on policy, practice, public opinion, and, in this case, the lived experiences of people who are homeless. The article details the diverse activities of the CHRN (e.g., its methodologies, processes, and tools), highlighting the tension points, successes, and failures of particular approaches. Findings bring into view a) the CHRN’s role as a central connecting node, linking multiple and diverse individuals, institutions, and other networks; b) relations of reciprocity, which support ongoing interactivity between network members; and c) the changes (e.g., in research use) that network activities have influenced. Data suggest that the use of research evidence to co-produce “useable content” is a key indicator of network productivity.

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)0.010
metaresearch head score (Gemma)0.002
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 score0.711
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.509
GPT teacher head0.573
Teacher spread0.063 · 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 designTheoretical or conceptual
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

Citations4
Published2014
Admission routes4
Has abstractyes

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