Bibliographic record
Abstract
We report an experimental course in toxicology to bridge two disparate cultures (sciences and the humanities) taught by two teachers who had expertise in different domains, pharmacology (P.K.R) and literature (A.S.). Students were required to: learn the fundamentals of toxicology and demonstrate they had done so through creative writing (short stories, poems, plays) and reviews of films or books related to poisons. A blended learning format was used: i.e. a judicious mix of face‐to‐face and on‐line learning. 17 undergraduate students at the University of Calgary who took the course were enrolled in English, Fine Arts, Nursing, Kinesiology and Health Sciences. An excerpt from a detective story provided the trigger for a brief introduction to toxicology (P.K.R). A.S. guided students in the creative writing component. Evaluation included individual and group projects which were assessed by both instructors. Groups selected a poison (e.g. thallium, warfarin, hemlock, digitalis, ricin amongst others) and produced a creative work (play, short‐story, script). Individual submissions included a reflective journal, along with peer and self‐assessment, a film or book review, and a creative writing project in any genre. Students showed that they could critically assess information about the poisons used in their own projects and comment on the accuracy of the portrayal in movies and books.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".