Developing an Interdisciplinary, Distributed Graduate Course for Twenty-First Century Scientists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graduate programs have placed an increasing emphasis on the importance of interdisciplinary education, but barriers to interdisciplinary training still remain. We present a new model for interdisciplinary, cross-institution graduate teaching that combines the best of local teaching, distance learning, and experiential learning to provide students and faculty with a unique collaborative learning experience and interdisciplinary research skills. We summarize the lessons learned from a highly successful implementation of this course model in the new field of landscape genetics, which integrates concepts and methods from population genetics, landscape ecology, and spatial statistics. The distributed nature of the course allowed sections to be offered locally that would not have been offered otherwise because of the lack of complementary expertise at local institutions. Students gained hands-on experience in interdisciplinary, Web-based and international research collaboration with group projects. A final synthesis meeting was invaluable for course assessment, manuscript development for group projects, and professional networking.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it