Becoming a Scientist: PhD Workplaces and Other Sites of Learning
Bibliographic record
Abstract
Doctoral students have often been described as apprentices engaged in workplace learning. Further, assumptions are frequently made in the literature about the common nature of such learning experiences, e.g., in the sciences, research-related practices are learned in a lab within the supervisor’s program and team. A few recent studies of the science doctoral experience have challenged this view arguing such assumptions may overlook considerable variation. This longitudinal study, using frequently completed activity logs and an interview, reports on the research-related practices of twelve UK science doctoral students. The analysis, particularly of the logs, challenged some of the literature-based assumptions: students often chose to work in institutional offices, non-institutional sites and their homes rather than in labs; they did not necessarily engage regularly with a research team, nor were they necessarily engaged in a project directly linked to their supervisors’. That students chose not to work in traditionally assumed places suggests the importance of attending to: a) student agency, b) how research-related practices may be changing, and c) how sites of doctoral learning might need to be reconceived. As well, the findings suggest the value of non-traditional data collection methods in capturing variation in experience.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".