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Record W2604712974 · doi:10.24908/pceea.v0i0.6454

Enhancing outreach through the University of Ottawa Maker Mobile

2017· article· en· W2604712974 on OpenAlexaffvenueabout
François Bouchard, Hanan Anis, C. Laguë

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOutreachEngineering managementEngineeringTruckPolitical science

Abstract

fetched live from OpenAlex

The Maker Mobile program is a new modelfor outreach at the faculty of engineering atthe university of Ottawa that allows foryearlong delivery of high quality technologyworkshops to the community atlarge. Through the transportation of rapidprototyping technologies in a 12-foot orangecube truck, the Maker Mobile deliveredmore than 719 workshops and reachedmore than 14000 youth in the past year. Inparticular this program is helping teachersincorporate engineering into theirclassrooms through hands on designactivities. This fosters interest forengineering while helping recruitmentefforts. The Maker Mobile is also helpingthe faculty develop relationships with highschools, teachers and school boards for thedevelopment of new spin off outreachinitiatives. The Maker mobile builds on asolid foundation for outreach at the facultyof engineering. Three important factorshave contributed to the development of astrong foundation for our outreach program.These factors include developing processesthat ensure sustainability and scalability, astrong association to the institution, whichcreates demand for programs and aninternal support structure that ensuresprograms have the necessary resources toscale.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations1
Published2017
Admission routes3
Has abstractyes

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