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Record W4312736307 · doi:10.5406/21520542.36.3.03

Empowering Students for Future Work and Productive Citizenry Through Entrepreneurship Education

2022· article· en· W4312736307 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

VenuePublic Affairs Quarterly · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEntrepreneurshipCuriosityProactivityCreativityAdaptabilityPsychological resilienceWork (physics)EmpathyPsychologyPublic relationsResilience (materials science)PedagogyPolitical scienceEngineering ethicsSociologySocial psychologyManagementEngineeringEconomics

Abstract

fetched live from OpenAlex

Abstract Public policy makers are calling for all university students to learn entrepreneurial competencies to prepare them to be productive citizens in an unpredictable future. Far more than simply starting up businesses, entrepreneurship is increasingly seen as a student-centric pedagogical technique (teaching through entrepreneurship) for helping students learn desperately needed foundational skills and attitudes such as curiosity, creativity, opportunity spotting, grit, resilience, proactivity, adaptability, empathy, self-efficacy, motivation, and tolerance for uncertainty and risk. This article describes generational trends that make this education increasingly important and provides a Comprehensive Framework for Entrepreneurship Education (CFEE) to help implement best practices to achieve measurable Assurances of Learning (AoL) results at the institutional, degree program, and individual course levels.

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)0.000
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 score0.634
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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