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Record W3000934097 · doi:10.24908/pceea.vi0.13791

TRANSFERABLE SKILLS: FROM WORK TO SCHOOL

2019· article· en· W3000934097 on OpenAlexafffundvenue
Haaniyah Ali, Jeffrey Harris

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Leadership and Innovation
Canadian institutionsYork University
FundersYork University
KeywordsTransferable skills analysisSkills managementTransferabilityConsistency (knowledge bases)Communication skillsDiligenceLife skillsWork (physics)PsychologyEmphasis (telecommunications)Term (time)PedagogyMathematics educationMedical educationComputer scienceEngineeringPolitical scienceHigher educationSocial psychologyMedicine

Abstract

fetched live from OpenAlex

The focus of this project is on finding key skills that have been developed and/or transferred to the workplace from previous school experience, and how these relate back to a student’s school term. Following a phenomenological approach, this paper explores three case studies and tracks various skills from their coop term into their school term. The most transferable skills were communication, time management, organization, responsibility and problem solving. Some students also specified skills such as diligence, focus and the need for initiative as vital for a successful work placement. One consistency was that students did not find that their technical skills transferred between terms, but rather that there was far more emphasis and transferability of general skills. Therefore, general skills were the most transferable, both to and from the workplace. Finally, students mentioned the applications of skills to clubs, indicating the importance of extracurriculars in a student’s educational experience.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.004
Scholarly communication0.0040.004
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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 designNot applicable
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

Citations1
Published2019
Admission routes3
Has abstractyes

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