Drivers in the generation of university students' job preferences: an empirical study of person–organisation fit in social enterprises
Bibliographic record
Abstract
Purpose The objectives are to show how professional orientation towards cooperatives, as a universal model of social enterprise, stems from a different fit to professional preferences for other kinds of business forms; and to show how specific training has an impact on the change in professional orientation. Design/methodology/approach The methodological approach was structured based on two stages. In the first stage, a cluster analysis was used on the responses obtained from a sample of Canadian university students, while in the second stage, experimentation was used to analyse how the professional orientation of students that did not have a preference for cooperatives changed after receiving specific training. Findings The results reveal how cooperative work preferences are found in students with symbolic values compatible with the essence of the cooperative model, their knowledge being the catalyst for the person–organisation fit. Furthermore, a change in professional orientation in most subjects stems from training, demonstrating the crucial role this has for individuals to be able to assess their match with the different business models. Originality/value Research on the pairing of university graduates with companies has had a strong development in recent years given the relevance of employability as a guiding principle of university education. This is the first empirical work linking person–organization fit in the formation of job preferences applied to social enterprises. The results have implications for universities, the cooperative sector and political decision-makers, who will have to improve the visibility and awareness of cooperatives to increase their attraction as an employment provider.
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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 | 0.004 | 0.010 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".