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Record W3011360708 · doi:10.1109/fie43999.2019.9028515

Transdisciplinary Design Build Studio Course in Collaboration with Shoal Lake 40 First Nation: Development and Assessment

2019· article· en· W3011360708 on OpenAlexaffabout
Jillian Seniuk Cicek, Marcia Friesen, Shawn Bailey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStudioCourse (navigation)ShoalComputer scienceArchitectural engineeringEngineeringOceanographyGeologyTelecommunications

Abstract

fetched live from OpenAlex

This Innovative Practice Full Paper presents a transdisciplinary Design and Build studio course offered by the Faculties of Engineering and Architecture in a large research university in Western Canada in collaboration with Shoal Lake 40 First Nation community. The objectives of the course are to develop and execute design explorations while seeking to clarify relations between architectural and engineering criteria and the rural/natural environments in an Indigenous community. This paper describes the interpretation of the course as “education for sustainability,” and the implications of boundary crossing. The mixed methods research case study design to assess the delivery of education for sustainability is described, as well as the application of boundary crossing theory as a framework through which to view the course, design course activities, and assess course content, delivery, and student learning.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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 designObservational
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

Citations5
Published2019
Admission routes2
Has abstractyes

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