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Record W3021036464 · doi:10.53379/cjcd.2020.25

Preparing Undergraduate Students for Tomorrow's Workplace: Core Competency Development Through Experiential Learning Opportunities

2020· article· en· W3021036464 on OpenAlexaffvenue
Elizabeth Bowering, Christine Frigault, Anthony R. Yue

Bibliographic record

VenueCanadian Journal of Career Development · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsExperiential learningCore competencyCore (optical fiber)Experiential educationPsychologyMedical educationPedagogyEngineering ethicsEngineeringBusinessMedicineMarketing

Abstract

fetched live from OpenAlex

The rapid evolution of today’s workplace requires employees to possess a diverse set of sophisticated cognitive and psychological competencies, thus prompting post-secondary institutions to reconsider not only what is taught but why and how. Our paper proposes a three-faceted model of core competencies that undergraduate students can develop through participation in experiential learning (EL). We describe three EL opportunities at Mount Saint Vincent University that engage students in authentic experiences and encourage critical reflection: service learning (SL) in the Department of Psychology, co-operative education in the Bachelor of Public Relations (BPR) program, and a co-curricular recognition program (CCR) in Career Services. We also provide supporting evidence that EL facilitates the development of core competencies and career readiness. We conclude with recommendations that may help post-secondary institutions better prepare students for the competency-based workforce of tomorrow.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.152
GPT teacher head0.322
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2020
Admission routes2
Has abstractyes

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