Testing a Model of Co-Op Students' Conversion Intentions
Bibliographic record
Abstract
Due to increased competition for talent, employers often look to convert co-op employees to full-time hires. The purpose of this paper was to conceptualize and test a model of co-operative education (“co-op”) students’ conversion intentions (i.e., plans to become a full-time member of the organization). Perceived work term quality (learning, impact, and relatedness) is proposed to influence conversion intentions serially through work engagement (feeling of vigor, dedication, and absorption at work) and organizational commitment (strong bond with the employer). The model is tested with data collected from co-op students (n =1,364) at a Canadian university. As predicted, results suggest that perceived work term quality affects conversion intentions both directly and indirectly through work engagement and organizational commitment. This study is the first to examine potential contributions of the perceived quality of co-op students’ work term experiences to students’ plans for becoming a member of the organization. As such, it has important implications for how host organization members such as supervisors can design and deliver co-op work term experiences to leverage the benefits of participating in co-op
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".