<i>Narratives of Discovery</i> as a catalyst for translational science education and training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it