Enhancing Employability Skill Sets: The Obligation of Community Colleges to be Greater Than the Sum of Their Parts
Bibliographic record
Abstract
The pressure upon post-secondary institutions in Ontario to address the persistent gap between the employability skill sets of their graduates and the changing needs of the modern workplace has never been greater. Forces such as the complexities of participating in a globally competitive economy, and advancements in information and communication technologies have shifted workplace expectations. Parents, students, and employers want to be assured that a diploma is indicative of the full range of skill sets necessary to achieve entry into a chosen occupation. The case method of analysis was used to examine one college’s quality assurance strategies for teaching and assessing Essential Employability Skills (EESs). Concerns with the validity for some of the EESs and the resulting issues with the reliability of curriculum mapping matrices were identified. Fink’s Integrated Course design (2013) is proposed as a strategy to address the gap between the employer expectations and what is taught and assessed in a community college. The establishment of a campus-wide working group to advance the EESs agenda, increased collaboration with Program Advisory Councils, and increased training are some of the solutions proposed. This problem of practice is considered through Bolman and Deal’s Four Frame Model (2013) and examines the pragmatic obstacles that thwart post-secondary efforts to equip their graduates with these employability skills. This Organizational Improvement Plan utilizes Cawsey, Deszca and Ingols’s Change Path Model (2016) as a guiding framework.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".