29. Using Skills Portfolios in Fourth-Year University Transition to Work Courses
Bibliographic record
Abstract
‘Transition from school to work’ courses are an excellent way to help fourth-year university students as they complete their studies and prepare for the world of work. In this paper we present The Bases of Competence (Evers, Rush, & Berdrow, 1998), a model of the advanced skills used by university graduates in the workplace. The model consists of four groupings of skills (base competencies): Managing Self, Communicating, Managing People and Tasks, and Mobilizing Innovation and Change. Each base competency consists of four or five more specific advanced skills (e.g., Mobilizing Innovation and Change consists of ability to conceptualize, creativity, risk-taking, and visioning). The base competencies and the skills within each base serve as the core of the skills that make up the skills portfolios students complete in the transition courses conducted at the University of Guelph and the University of Guelph-Humber. Students reflect on and report behaviours related to each skill based on their education, life, and work experiences. The portfolio also includes a résumé, cover letter, and other elements related to career development and work search. The portfolio comprises fifty percent of the course; the remainder is taken up with a project and presentation aimed at capping the student’s undergraduate experience with eyes to the future and enhancing under-utilized oral communication skills.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".