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Forget to clean up when you're done.

2019· article· en· W4235661833 on OpenAlexaboutno aff
Milena Radzikowska, Stan Ruecker, Jennifer Roberts-Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Software deploymentMateriality (auditing)Embodied cognitionSpace (punctuation)Argument (complex analysis)Computer scienceSession (web analytics)Mathematics educationPedagogyPsychologySociologyAestheticsArtificial intelligence

Abstract

fetched live from OpenAlex

In most university settings the rooms are scheduled centrally in such a way that even moving tables and chair configurations can prove problematic. Because different faculty use the space for different purposes, common courtesy and institutional exigency both dictate that classrooms should be reset to neutral at the end of each session. However, from the perspective of design pedagogy this otherwise beneficial practice becomes problematic. For design students there is a strong benefit in the material culture of the design space being intrinsically modelled in the classroom. We therefore offer an alternative argument to the conventional deployment of classroom space, based on three case studies from institutions in the USA and Canada where the opportunity has existed for various forms of material permanence in the classroom setting. The benefits to the students of leveraging materiality and material persistence in the classroom include: • pedagogical benefits—having ready-to-hand reminders of core principles that are represented by objects; • efficiency—classroom conditions of material persistence allow students to pick up from where they left off without wasting time at the beginning of each class in re-establishing their presence in the work; • opportunities for mental reset—transitional spaces from the generic to the creative can remind students that they are entering a different kind of pedagogical environment; and • more accurate discipline representation—the ability to be immersed in works in progress rather than completed items on display. Finally, and perhaps most importantly, embodied classroom environments support students more holistically by remembering that makers have brains and bodies that need physical, psychological, and emotional nourishment.

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)0.003
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.068
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.005
Scholarly communication0.0070.011
Open science0.0020.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0680.076

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 head0.254
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 designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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