Unravelling the process of idea generation and assessment during the <scp>PhD</scp> trajectory: A case study approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it