“Fact‐Ways”: Teaching Cellular Signaling Using The Nobel Archives
Bibliographic record
Abstract
Freshmen biology courses often present facts as given rather than constructed. Rarely then do students recognize the time, effort, and investment that have produced those facts and sense the excitement and fascination of scientific research. In a large‐enrolment first‐year course (>180 students), we used active learning strategies to give health sciences students an appreciation of the process of scientific discovery by focusing on Nobel Prizes given to seminal discoveries in cellular signaling. Groups of students were asked to explore the works of a specific Nobel Laureate and their learning was assessed in one or more different formats that ranged from an individual written examination to different group activities (simulated interviews, generation of an open‐ended problem‐solving exercise, or a take home project). On a 10‐point scale, 619 students, over a period of 6 years, rated the learning value of the projects as follows: mean 7.2, median 7, mode 8, and range 1–10. The group projects received slightly higher rating than the individual exams. Deconstructing facts, rather than simply memorizing them, makes learning richer. Support or Funding Information None
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".