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Record W2618905166 · doi:10.18260/1-2--16380

Design Of The Learning Environment For Inclusivity: A Review Of The Literature

2020· review· en· W2618905166 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.

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMathematics
TopicMathematics Education and Programs
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInclusion (mineral)Diversity (politics)Equity (law)Underrepresented MinorityFace (sociological concept)PopulationWork (physics)Learning environmentEngineering educationPedagogyCultural diversityMathematics educationEngineering ethicsPsychologySociologyEngineeringMedical educationPolitical scienceEngineering managementSocial scienceMechanical engineeringMedicine

Abstract

fetched live from OpenAlex

Retention, especially of under-represented populations through the first year university, is an ongoing concern in engineering programs. While this is a very complex issue, one of the aspects of retention that is being studied is the barriers to inclusion that some students feel when they enter university. There are many programs aimed at helping freshman acclimatize to the university environment and the issue of inclusivity is becoming more pronounced as we strive to increase and then maintain the diversity of our student population in engineering programs.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.825
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.161
GPT teacher head0.402
Teacher spread0.241 · 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

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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