Matters of taste: bridging molecular physiology and the humanities
Bibliographic record
Abstract
Taste perception was the focus of an undergraduate course in the health sciences that bridged the sciences and humanities. A problem-based learning approach was used to study the biological issues, whereas the cultural transmutations of these molecular mechanisms were explored using a variety of resources (novels, cookbooks, and films). Multiple evaluation procedures were used: problem summaries and problem-solving exercises (tripartite problem-solving exercise) for the problem-based learning component and group tasks and individual exercises for the cultural issues. Self-selected groups chose specific tasks from a prescribed list of options (setting up a journal in molecular gastronomy, developing an electronic tongue, designing a restaurant for synesthetes, organizing a farmers' market, marketing a culinary tour, framing hedonic scales, exploring changing tastes through works of art or recipe books, and crafting beers for space travel). Individual tasks were selected from a menu of options (book reviews, film reviews, conversations, creative writing, and oral exams). A few guest lecturers (wine making, cultural anthropology, film analysis, and nutritional epidemiology) added more flavor. The course was rated highly for its learning value (8.5 ± 1.2, n = 62) and helped students relate biological mechanisms to cultural issues (9.0 ± 0.9, n = 62).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".