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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 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.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.152
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