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Record W3006242543 · doi:10.1177/8756972819896697

Exploring Student Perceptions of Their Readiness for Project Work: Utilizing Social Cognitive Career Theory

2020· article· en· W3006242543 on OpenAlexaff
Ruben Burga, Joshua LeBlanc, Davar Rezania

Bibliographic record

VenueProject Management Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsWorkforceFraming (construction)Work (physics)PerceptionProject managementSocial cognitive theoryKnowledge managementPsychologyCognitionProject management trianglePublic relationsSociologyManagementEngineeringPolitical scienceSocial psychologyComputer science

Abstract

fetched live from OpenAlex

A changing labor market is leading to an increased prevalence in project work. In this study, we explore student perceptions of project work. We find that these emerging adults prefer leadership positions, are concerned with social values, and view project work as essential preparation for the workplace. Utilizing a social cognitive career theory lens, we find that the goals, interests, and self-efficacy beliefs of emerging adults align with the needs of project management, but there is a lack of technical knowledge on project processes. Framing the question from the viewpoint of students who will be entering the workforce at the end of their programs of study, we see that students embrace the concepts inherent in project work. The implication for human resource managers is that emerging adults believe that they can succeed in project work, but technical skills are needed to help them succeed in formal project management roles. We provide recommendations, discuss limitations, and suggest future research directions.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.143
GPT teacher head0.313
Teacher spread0.169 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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