<i>Narratives of Discovery</i> as a catalyst for translational science education and training
Bibliographic record
Abstract
Abstract New education and training opportunities are critical for the development of a diverse and highly skilled translational science workforce. In this special communication, the authors consider how Narratives of Discovery, an initiative to interview leading scientists about the sources of their creativity, can serve as a novel translational science teaching tool. Reporting on a project to map translational science principles onto nine Narratives of Discovery conducted to date, the authors demonstrate how translational science principles are manifested in the career trajectories of these scientists and propose that the narratives can serve as a formative model for trainees. Findings from systematic coding of the Narratives of Discovery suggest that the narrative format is particularly well suited to highlight translational science principles not well-addressed by existing education opportunities, including what it means for scientists to be creative and innovative, use bold and rigorous approaches, and prioritize diversity, equity, inclusion, and accessibility. Offering excerpts from the published Narratives of Discovery and quotations from the scientists themselves, the authors aim to create space for continued conversation about how to best crystallize the concepts of translational science and advance translational science education and training initiatives.
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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 | 0.060 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".