Employer Perceptions of Co-curricular Engagement and the Co-curricular Record in the Hiring Process
Bibliographic record
Abstract
Throughout media channels, there have been concerns about a perceived job skills gap, which in turn have led to questions about the value of a university education. Canadian universities and colleges have developed the Co-Curricular Record (CCR) as a means to incentivize and recognize student engagement in co-curricular opportunities, which research has shown to positively impact student development, retention, and success (Astin, 1993; Chickering, 1969; Tinto, 1987). This study surveyed employers to explore current hiring practices, including the current value of candidate materials and hiring factors, desirable soft skills, and the perceived value of the CCR. This thesis explores the potential use of the CCR in the hiring process, and argues that the CCR can act as a translation tool, by elevating the value of co-curricular experiences in developing and articulating soft skills. This thesis also discusses current challenges and provides a series of recommendations and next steps.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".