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Record W3100039087 · doi:10.22215/etd/2019-13661

The Role of 3D Printed Objects Facilitating the Process of Mutual Communication and Collective Idea Generation Between Non-designers and Designers

2019· dissertation· en· W3100039087 on OpenAlexaffabout
Pablo Arzate

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsCarleton University
Fundersnot available
KeywordsIdeationCLARITYProcess (computing)Context (archaeology)Design processEngineering design processDomain (mathematical analysis)Human–computer interactionComputer scienceEngineering3d printedWork in processPsychologyManufacturing engineeringCognitive scienceMechanical engineeringOperations management

Abstract

fetched live from OpenAlex

Despite the remarkable technological improvements in 3D printing technology, there are diverse challenges that small scientific laboratories encounter when using such equipment to develop customized lab equipment. A major challenge is successfully transferring and incorporating the scientists' ideas during the design process due to their limited experience in design practices. This paper explores the role of 3D printers in the ideation process of design specifically in the context of Co-design environments with scientists. It is argued that 3D printed objects can support ideation by allowing enhanced engagement between participants, helping uncover important insights and increasing the team's idea generation process while enhancing the clarity of communicating these design ideas. A workshop was conducted to test this hypothesis at the University of Ottawa Laboratory of Cellular and Molecular Medicine with to demonstrate that the 3D printer could support non-designers in the ideation process by enabling different domain-specific types of interactions

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.508

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)0.0000.000

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 head0.015
GPT teacher head0.280
Teacher spread0.266 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2019
Admission routes2
Has abstractyes

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