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Record W4235895205 · doi:10.24908/pceea.v0i0.3770

SCHOLARLY DESIGN?

2011· article· en· W4235895205 on OpenAlexaffvenue
Marjin Eggermont, Colin McDonald

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNothingThe artsProcess (computing)Fine artEngineering ethicsSociologyEpistemologyWork (physics)Applied artsMathematics educationComputer sciencePsychologyEngineeringVisual artsPhilosophyArt

Abstract

fetched live from OpenAlex

Upon entering the School of Engineering from the practice-based Faculty of Fine Arts questions arose regarding the pedagogy of design theory versus the design work of first year engineering students. Fine Arts, in the past couple of years, has tried to enter higher levels of academia by starting to offer PhD programs that are practice-based. Often times these degrees have an aspect of theorizing practice into analysis and thereby equate the process of creating art to the process of scientific experimentation. If one looks at the area of scientific inquiry, which is according to Heidegger: ‘nothing less than the making secure of methodology over what ever is (nature and history)’, questions arise over whether theorizing practice into analysis is the right approach. By doing the latter, one calls into question whether practice by itself is valid and whether by creating a (somewhat) artificial analysis one diminishes the process of practice to a secondary activity. This paper will start by looking at research done in the area of Fine Arts to solve the ‘theorizing practice into analysis’ question by creating a situation where practice can be considered as more than mere experimentation. In addition there will be a discussion as to how these findings might be applied to Engineering Design.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.613
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.194
Teacher spread0.174 · 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 designNot applicable
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
Published2011
Admission routes2
Has abstractyes

Explore more

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