MétaCan
Menu
Back to cohort
Record W2794191495 · doi:10.1177/0020852317741679

Co-creation within hybrid networks: what can be learnt from the difficulties encountered? The example of the fight against blood- and sexually-transmitted infections

2018· article· en· W2794191495 on OpenAlexaffabout
Nassera Touati, Lara Maillet

Bibliographic record

VenueInternational Review of Administrative Sciences · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsInstitut National d'Excellence en Santé et en Services SociauxÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsPublic relationsSet (abstract data type)Action (physics)Community organizationControl (management)Knowledge managementBusinessSociologyPolitical scienceProcess managementComputer scienceManagementEconomics

Abstract

fetched live from OpenAlex

This article analyses co-creation processes within hybrid networks. Specifically, it looks at a particular co-creation mechanism, in this case, a strategic community set up to test new ways of dealing with blood- and sexually-transmitted infections in Quebec. A strategic community is a temporary structure of inter-organizational collaboration, made up of professionals, first-level managers, general practitioners, representatives of community organizations, etc. tasked with generating, implementing and evaluating new ideas about the organization of services. The results of this study highlight the difficulties encountered as well as the issues related to these co-creation processes. Notes for practitioners The implementation of co-creation processes, involving public, private and community actors, has to contend with numerous challenges: (1) the enrolment of the stakeholders concerned by the complex issues; (2) the creation of places for discussion and experimentation involving actors who play an important role in the implementation of change; (3) the mobilization of tools and facilitators to develop a common vision; (4) the action stage to validate new ways of doing things.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.372
Teacher spread0.323 · 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 designObservational
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

Citations10
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueInternational Review of Administrative SciencesSame topicSocial Sciences and GovernanceFrench-language works237,207