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Record W3036337165 · doi:10.24908/pceea.vi0.14152

DESCRIPTION OF PRE-UNIVERSITY CODING WORKSHOPS RECRUITING FOR DIVERSITY

2020· article· en· W3036337165 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDiversity (politics)Coding (social sciences)Science and engineeringEngineering managementMedical educationEngineeringEngineering ethicsLibrary scienceComputer scienceSociologyMedicineSocial science

Abstract

fetched live from OpenAlex

Given the need to increase diversity in technical fields, the Schulich School of Engineering at the University of Calgary created an out-of-school coding workshop for pre-university students, now known as “Schulich Ignite.” Five of these innovative and hands-on workshops have been run since 2017 with the objective of increasing diversity in science and engineering. Since its inception, over 400 people have participated as mentees or mentors. In this paper, we describe the program as it started in 2017 and the four iterations it has gone through with focus on the recruitment techniques, organization, program delivery, and outcomes. We look at enrollment, exposure, and diversity in the program. From the preliminary results, we propose areas of future research for delivering and researching pre-university engineering workshops.

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.

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)0.002
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 score0.194
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.023
GPT teacher head0.204
Teacher spread0.181 · 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