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Record W3026805004 · doi:10.22215/etd/2019-13868

From A Tool For Making To A Tool For Thinking: Investigation of the Potential of 3D Printing in Meaning-Making

2019· dissertation· en· W3026805004 on OpenAlexaff
Spase Janevski

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicCrafts, Textile, and Design
Canadian institutionsCarleton University
Fundersnot available
KeywordsMeaning (existential)3D printingProcess (computing)Set (abstract data type)EngineeringHuman–computer interactionComputer scienceEngineering drawingMechanical engineeringPsychology

Abstract

fetched live from OpenAlex

The advances in 3D printing technology have an enormous potential to impact how designers learn and develop practical knowledge during the design process.The purpose of this research study was to investigate the potential of 3D printing as a tool for meaningful making.Through investigation of handmade objects and their qualities, this study set out to determine how designers can invent 3D printed objects that make sense to stand alongside handmade objects.Data for the study was obtained using both ethnographic and design research methods, including: an observation, experimental studies and a survey.Results showed that 3D printing has a potential not only to develop meaningful outcomes, but also to drive design processes that make sense to designers.Through engagement and understanding of 3D printing machine, designers can develop not only practical knowledge, but also an understanding of the meaning of their making.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.012
Scholarly communication0.0050.006
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.270
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes1
Has abstractyes

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