Storytelling for Translational Research Impact
Bibliographic record
Abstract
ABSTRACT Translational research converts research knowledge into practical wisdom for a community (What is Translational Research, n.d.). Storytelling for translational research means that the researcher knows the audience, crafts a narrative, sticks to the plot, and imparts wisdom in a meaningful way – all elements of a good story from a good storyteller. In this hybrid panel and workshop, led by Stephann Makri and other members of the ASIS&T Research Engagement Committee, our successful researchers/storytellers will illustrate how a good translational research impact story is structured. Then, our storytelling experts will help participants craft their own research narratives to put translational research storytelling into practice for their own research stories. Dr. Kate McDowell, panelist and storytelling expert, teaches both storytelling and data storytelling courses, and is the 2022 recipient of the ASIS&T Outstanding Information Science Teacher Award. She states: “When research successfully translates into legislative or policy changes, it always comes down to a shared narrative experience. The story emerges in the dynamic interaction between the teller and the audience.” The aim of this session is to create confident storytellers.
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.
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 | 0.048 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".