Developing as a post-PhD researcher: agency and feedback in construction of grant funding success ( <i>El desarrollo del investigador posdoctoral: agencia y feedback en la preparación de propuestas de financiación exitosas</i> )
Bibliographic record
Abstract
Grant funding is critical to building a sustained research career. Yet in this climate of academic performativity little is known about how individual academics make their contribution to knowledge fundable. This in-depth longitudinal case study explores an approach evolved by an experienced Canadian scientist whose work is recognized for both scholarly and societal impact. We demonstrate how, over ten years, his agentive participation in academic interactions has contributed to continual development of his research and its funding through: his seeking and engaging with feedback; network-wide view of feedback sources; and drawing on feedback to inform his writing and research thinking. The article suggests a developmental view of the process in which, over time, researchers conceive, expand and draw on their feedback networks. Locating funding success within the social dimensions of knowledge and researcher development, the study sheds light on the role of agency and experiential learning in enabling contributions to frontline knowledge. Results suggest a novel, encompassing way for post-PhD researchers to build towards sustained grant funding.
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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 | 0.082 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.029 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".