Professional imprinting mechanisms in the doctoral trajectory: Impact on researcher identity diversity
Bibliographic record
Abstract
Shaping one's professional identity is a complex process that starts early on in the professional career and is influenced by many factors along the way. An important process in professional identity formation is professional imprinting. In socialization theory, professional imprinting refers to how individuals adjust behavior and beliefs to fulfill expectations from their working environments and achieve a feeling of belonging during sensitive periods. In this study, we turn to the academic setting, which is characterized by high researcher identity heterogeneity and thus can give us insights into the dynamics of professional identity development. Professional imprints during doctoral training lead to permanent characteristics in one's researcher identity. To investigate professional imprinting and its mechanisms, we conducted a qualitative study involving interviews with 16 PhD students and their supervisors (16 professors and 4 post-docs) within the setting of an EU-funded project. We identify the imprinting mechanisms that shape a researcher's identity during a sensitive period. Our study offers valuable insights for managers and policy makers about the role of supervisors or supervising managers in the development of the professional identities of junior colleagues and about the future career trajectories of people entering academia and industry. • Supervisors play a crucial role in shaping PhD students' researcher identities through professional imprinting. • Scientific, entrepreneurial, and transversal imprinting mechanisms are key factors influencing PhD students' identity development. • Differences in researchers' ‘taste for science’ and ‘taste for commercialization’ contribute to varied hybrid researcher identities. • We propose a PhD student management tool for supervisors, aiming to support careers in and outside academia.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".