MétaCan
Menu
Back to cohort
Record W3016046832 · doi:10.1096/fasebj.21.5.a217

The Poisoned Pen: Toxicology and Creative Writing

2007· article· en· W3016046832 on OpenAlexaffabout
P. K. Rangachari, Aruna Srivastava

Bibliographic record

VenueThe FASEB Journal · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsUniversity of CalgaryMcMaster University
Fundersnot available
KeywordsPsychologyCreative writingMedical educationMathematics educationMedicineVisual artsArt

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.263
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicAcademic Writing and PublishingFrench-language works237,207