Creating entrepreneurial networks : Commercialisation of research, mobility and collaboration during PhD education
Bibliographic record
Abstract
The universities are expected to contribute to the society in manifold ways; their main tasks include supplying the labour market with educated workers, developing scientific knowledge, both basic and applied, and recently also supporting entrepreneurial initiatives and commercialisation of research results. PhD education constitutes a considerable part of many universities’ activities and PhD students perform a large share of research. Yet there are few studies concerned with PhD students’ possibilities to commercialise research results or the university context supportiveness in this regard. Therefore, this paper investigates PhD students’ views on commercialisation and perceived grade of support from various levels of the university hierarchy. Moreover, the extent of mobility and external collaborations during PhD education and their correlations with opinions of PhD students are studied. These aspects are studied through analysis of data from a survey of 465 PhD students at Linkoping University, Sweden.The results show that PhD students are on average slightly positive towards commercialisation of research results, although there are differences between various faculties. The university context is perceived as slightly supportive, except for the department and division levels at the faculties of Arts & Sciences (incl. Educational Sciences) and Health Sciences. A majority of PhD students are involved in external collaborations as a part of their PhD education, while a quarter have been spending a part of their PhD studies at another organisation. PhD students’ views on commercialisation are more connected to the direction of mobility than to mobility per se, while for external collaboration interest in commercialisation is lowest amongst those not involved in collaboration at all.
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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 | 0.029 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".