Early career researcher challenges: substantive and methods-based insights
Bibliographic record
Abstract
Navigating academic work as well as career possibilities during and post-Ph.D. is challenging. To better understand these challenges, since 2010, we have investigated the experiences of early career scientists longitudinally using a range of qualitative data collection formats. For this study, we examined the experiences of four students and four postdocs to address two questions. The first, a substantive one, asked about the challenges early career researchers experienced and their efforts to be agentive in response. The second methods-based question examined whether different data collection formats, weekly activity logs completed monthly and annual interviews, might contribute different insights into challenges and responses to them. In fact, the subtle differences that emerged from each of the data sources enabled us to substantively characterize different kinds of challenges and different patterns of response. Individuals were generally successful in managing day-to-day and short-term research-related challenges (largely reported in the logs) and developing coping strategies for existential challenges (reported in the logs and interviews). But structural issues (largely reported in the interview) were less tractable. The findings suggest that combining distinct data collection methods may better capture variation in experience – in this case, challenges and responses – than single formats alone.
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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 | 0.219 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".