The UNSIN project: exploring the molecular physiology of sins
Bibliographic record
Abstract
Although active learning works, promoting it in large undergraduate science classes is difficult. Here, three students (F. Naji, L. Salci, and G. Hoit) join their teacher (P. K. Rangachari) in describing one such attempt. Two cohorts in a first-year undergraduate biology course explored the molecular underpinnings of human misbehavior. Students were divided into 18 groups and randomly allotted to deal with one of the four deadly sins: sloth, gluttony, lust, and wrath. Students were expected to read primary sources to devise molecular ways to counter these sins. Group progress was monitored over the 12-wk period by the preceptor (P. K. Rangachari) at scheduled intervals. A single randomly selected student was questioned about the work done, and future directions were provided by the preceptor. At the end of the term, randomly selected students defended their group's approaches to the entire class. A final written report was graded. The following multiple target molecules were considered for each sin: gluttony (cholecystokinin, ghrelin, GABA, leptin, peptide YY, neuropeptide Y, and the melanocortin 4 receptor); sloth (dopamine, glutamate, GABA, and orexin); wrath (serotonin, GABA, glutamate, and corticotropin-releasing hormone receptor 2); and lust (prolactin, testosterone, oxytocin, dopamine, and estrogen). Students noted that the project provided a valuable learning experience, and the random selection approach gave students a greater sense of responsibility to their group. The project helped students hone their skills at searching, synthesizing, sharing, and presenting information, fostered group interactions, and provided a solid knowledge base for subsequent courses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".