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Record W4292337426 · doi:10.1787/08f79edd-en

Co-creation during COVID-19

2022· paratext· en· W4292337426 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOECD science, technology and industry policy papers · 2022
Typeparatext
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
FundersUniversity of QueenslandAgencia Nacional de Investigación y DesarrolloMinistry of Education, Culture, Sports, Science and TechnologyRIKENVlaamse regeringEuropean CommissionGovernment of CanadaAustralian GovernmentCommonwealth Scientific and Industrial Research OrganisationInnovation, Science and Economic Development Canada
KeywordsCoronavirus disease 2019 (COVID-19)Government (linguistics)PandemicCo-creationKey (lock)Civil societyProcess (computing)Political science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public relationsBusinessKnowledge managementMarketingComputer sciencePoliticsMedicineComputer security

Abstract

fetched live from OpenAlex

Co-creation -the joint production of innovation between combinations of industry, research, government and civil society -was widely used to respond to the challenges raised by the COVID-19 pandemic. This paper describes 30 COVID-19 co-creation initiatives from 21 countries and three international cases. The template focuses on initiatives' core characteristics, including information on key co-creation partners and their contributions, key outcomes as well as the initiatives' size. The comparative evidence gathered through interviews with case study initiative leaders also describes what co-creation instruments were used, how networks leading to the collaboration were built, what type of cross-disciplinary co-operation took place, and what role governments played in the process and the procedures adopted to deal with the COVID-19 "exceptionality", including the urgency of producing implementable solutions. The information gathered provides a basis for analyses on cocreation initiatives during COVID-19 and for drawing potential policy implications.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.008
Science and technology studies0.0050.006
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.317
Teacher spread0.294 · 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