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Record W2906213340

Creating space for diverse design culture in first year studio

2018· article· en· W2906213340 on OpenAlexaff
Maya Desai, Angelika Seeschaaf Veres, Nancy E. Snow

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsStudioDiversity (politics)Context (archaeology)Cultural diversityPedagogyDesign studioSpace (punctuation)SociologyHigher educationPsychologyVisual artsGeographyAnthropologyPolitical scienceArtComputer science
DOInot available

Abstract

fetched live from OpenAlex

At the onset of the pilot study, the authors collected a small sample of data on current teaching and learning practices at OCAD University with a focus on 'cultural diversity' within the studio classroom. The intent was to investigate the current state of 'cultural diversity' in studio practice seen from two perspectives: faculty and students. The first part of the study asked the question “What are the current efforts on the part of faculty and what, if any, are the design-specific-pedagogical approaches they use that relate to ‘cultural diversity’?” See the conference proceedings from NCBDS 2017 [http://ncbds.la-ab.com/33_Proceedings.pdf]. While faculty interviews uncovered predominant themes and levels of faculty engagement in relation to 'cultural diversity', the question arose as to whether or not students’ expectations and experiences aligned in similar ways. Therefore, this paper seeks to address the question, "What are student experiences and expectations in context to ‘cultural diversity’ with respect to their studio-based learning in first-year-design education?"

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)0.009
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.007
Scholarly communication0.0120.003
Open science0.0020.012
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.148
GPT teacher head0.318
Teacher spread0.170 · 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 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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