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Record W4308709577 · doi:10.24908/pceea.vi.15976

Leading Large Scale Innovation: Building Institutional Flexibility

2022· article· en· W4308709577 on OpenAlexafffundvenueabout
Suzanne M. Kresta, Vince Bruni‐Bossio, Nancy J. Turner

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsContextualizationFlexibility (engineering)Experiential learningScale (ratio)IndigenousEngineering managementProcess (computing)AppropriationChange management (ITSM)Knowledge managementEngineeringManagementSociologyPedagogyComputer scienceOperations managementGeography

Abstract

fetched live from OpenAlex

RE-Engineered was launched at the University of Saskatchewan in 2021/22. It was designed to build community among our first-year engineering students with modularized courses, full integration across all learning outcomes and courses, competency-based assessment, introduction to 4 sciences instead of the usual 2, an Indigenous Cultural Contextualization module, and replacement of final exams in December with a week of experiential learning days across 5 engineering disciplines. The scale of the changes envisioned by the first-year team (Sean Maw and Joel Frey) was so large that it impacted most institutional support units and had substantial operational and teaching practice change requirements for two colleges.
 Over the four-year design process, it became clear that the curricular design required a parallel and intentional process of broad organizational change for successful implementation. From this realization sprung the Change Management Committee (CMC). This group has leveraged resources (financial, human, expertise), influenced key decision makers on campus, and facilitated deep organizational change.

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)0.000
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: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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
Published2022
Admission routes4
Has abstractyes

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