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Record W4210412180 · doi:10.1002/iis2.12883

Participatory design as a tool to foster innovation: A case study of an aviation company

2022· article· en· W4210412180 on OpenAlex
Golnoosh Torkashvand

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

VenueINCOSE International Symposium · 2022
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsBrainstormingSession (web analytics)Citizen journalismParticipatory designAviationProcess managementParticipatory action researchFocus groupPower (physics)EngineeringKnowledge managementEngineering managementBusinessSociologyComputer scienceMarketingOperations management

Abstract

fetched live from OpenAlex

Abstract Participatory design and co‐creation have recently been considered as an effective approach in fostering innovation. Traditionally, tools such as brainstorming, focus groups and discussion boards were dominant in guiding discussions around innovation and solutions. While each of these tools consider great advantages in fostering innovations, there are sometimes drawbacks in conducting them in an unbiased way. This paper aims to provide the results and learnings from a participatory session called Group Elicitation Method (GEM) to co‐create future of business jets in Embraer company. In general, this experiment finds GEM as a very effective tool for consensus seeking, in the contexts where participants have diverse backgrounds and power balance is not symmetrical. The project was conducted as part of the author's PhD research on passenger experience.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.342
Teacher spread0.274 · 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