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Record W4402104879 · doi:10.55016/ojs/jet.v34i1.52612

The Employability Skills Discourse: A Conceptual Analysis of the Career and Personal Planning Curriculum

2018· article· en· W4402104879 on OpenAlexaffabout
Emery J. Hyslop -Margison

Bibliographic record

VenueJournal of educational thought. · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEmployabilityCurriculumPedagogySociologyPsychologyCareer planningMedical educationMathematics educationEngineering ethicsEngineeringMedicine

Abstract

fetched live from OpenAlex

The current focus on employability skills in Canadian public schooling raises important conceptual questions regarding this instructional approach to vocational education. In British Columbia, the Career and Personal Planning (CAPP) curriculum, introduced into secondary schools in 1995, reflects the growing trend toward skills education as a way to enhance the occupational relevance of schools. The career preparedness component of CAPP commits two fundamental category mistakes in its classification of employability skills both with potentially serious consequences for education. First, by incorrectly conflating distinct categories of concepts under the general rubric of generic skills, the contextual understanding, background know ledge, and epistemic attitudes required to achieve certain desired cognitive competencies are disregarded. Secondly, CAPP categorizes attitudes, values, and dispositions as skills and, in so doing, obscures important ethical distinctions between the contentious area of values education and basic skills instruction. By employing examples from both CAPP and the Conference Board of Canada's Employability Skills Profile (ESP), a mandatory supplement to the former program, this paper reveals how these category mistakes may prevent students from achieving program objectives, and circumvent important moral issues concerning the conveyance of values and attitudes to students.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.388
Teacher spread0.359 · 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 teacher head, not a consensus.

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

Citations2
Published2018
Admission routes2
Has abstractyes

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