The Employability Skills Discourse: A Conceptual Analysis of the Career and Personal Planning Curriculum
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".