Forensic Science and Cultural Anthropology: Embracing Complexity in an Interdisciplinary Classroom Based Exercise
Bibliographic record
Abstract
Integrating interdisciplinary learning into courses provides challenges as well as opportunities for deepening nuanced learning for both students and instructors. We present an interdisciplinary problem-based learning case study conducted in the classroom at a public university in the state of Alabama. Forensic science graduate students and undergraduate cultural anthropology students from two different classes were brought together to analyze and discuss a fictional scenario about a drug overdose. Students were asked to assess the situation and develop an integrated solution to the problem. In classroom discussion and in reflective feedback obtained afterwards, we found that many students from the two disciplines were initially resistant to the idea of working together because of differing methods and attitudes toward bias. However, by the end of the exercise, the students appreciated the potential for interdisciplinary collaboration in creating effective integrated policies related to drug regulation and enforcement. For student interaction, we found that classroom-level interdisciplinary exercises would be most effective when students understand the concept of interdisciplinarity and have insight into the other discipline prior to group discussions. Methodologically, our findings suggest that interdisciplinary education can be successfully implemented on a small scale, without requiring significant time commitments or institutional resources.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".