Unravelling the process of idea generation and assessment during the <scp>PhD</scp> trajectory: A case study approach
Bibliographic record
Abstract
Abstract The three missions of universities are education, research, and knowledge/technology transfer. At the micro‐level of the research and knowledge/technology transfer mission, we position researchers, as individuals who decided to pursue a scientific career in academia, with the PhD as the starting point. While existing literature acknowledges the supervisor's significance during this process from dependency to autonomy, this paper advocates for a closer examination of external factors such as the network, supervisor's experience, and work environment in idea generation. Ideas in this context encompass both curiosity‐driven and entrepreneurial concepts, often evolving from one to the other. Our research builds upon the theory of opportunity identification, drawing parallels between ideas and opportunities. The research asserts that PhD students primarily rely on their networks for idea generation due to limited prior knowledge and experience. Our findings underscore the dynamic interplay between PhD students, supervisors, and networks in the process of idea generation, advancing a comprehensive framework encapsulating the multifaceted influences on the trajectory from idea generation to execution in the context of PhD education. The framework is based on empirical evidence from a qualitative case study comprising 16 PhD students in a European H2020 project in the field of Photonics, illuminating the intricate relationship between supervisors' orientations (entrepreneurial or curiosity‐driven) and the types of ideas generated by PhD students. Practical implications highlight the need for tailored support and resources to foster independent research capabilities among PhD students, considering individual variations in supervisory support and networking opportunities.
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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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".