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Record W4247161072 · doi:10.24908/pceea.vi0.13749

THE ROLE OF MAKERSPACES IN INCLUSIVITY IN ENGINEERING

2019· article· en· W4247161072 on OpenAlexafffundvenueabout
Justine Boudreau Boudreau, Hanan Anis

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInclusion (mineral)CornerstoneFeelingEngineering educationTest (biology)Mathematics educationPsychologyFunction (biology)Space (punctuation)EngineeringMedical educationComputer scienceEngineering managementMedicineSocial psychologyGeography

Abstract

fetched live from OpenAlex

The University of Ottawa Faculty of Engineering is home to multiple rapid prototyping facilities and entrepreneurship spaces. These include a makerspace, a machine shop and a design space for any student to use free of charge. First- and second-year students also take courses in the Makerlab, a sister facility to the Makerspace, which introduces them to collaborative project-based learning, engineering problem-solving and prototyping in a cornerstone design course. Maker communities and makerspaces are known to be inclusive, welcoming and low-risk, high-reward environments. The objective of this paper is twofold: the first is to understand how strongly engineering students feel included in the making and engineering communities how those feelings vary as a function of different factors, and the second is to see if intervention through engineering design improves inclusivity. This analysis was done with Kruskal-Wallis tests. Factors considered were gender, year of study, program of study and country of origin. A baseline test was done at the beginning of the semester with the students using two different perceived group inclusion tools. A second test was then done at the end of the semester to determine if feelings of inclusion had changed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.001
GPT teacher head0.156
Teacher spread0.155 · 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 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

Citations2
Published2019
Admission routes4
Has abstractyes

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