Bibliographic record
Abstract
Interdisciplinary curriculum development is challenging in the sense that materials from more than one discipline have to be integrated in a seamless manner. A faculty member has to develop expertise in multiple disciplines in order to teach an interdisciplinary course, or the course has to be team-taught. Both approaches are difficult to implement. There are administrative issues, such as proportional posting of expenditures across departmental budgets for the courses taught collaboratively, or courses with students from multiple departments. This paper describes the development and teaching of a sequence of bioinformatics related interdisciplinary courses for incorporation into undergraduate biology curricula. Three courses were developed with collaboration between the Departments of Biology and Computer Science at Tuskegee University. Each course contains contents from different subjects, traditionally considered to be virtually independent of each other. The courses have contents from biology, computer science, statistics, mathematics and biochemistry. The first two courses, Introduction to Bioscience Computing and Biological Algorithms & Data Structures, cover the computing and computer science fundamentals necessary for the informed use of bioinformatics tools. The third is an introductory course in bioinformatics. The focus was on teaching the effective use of bioinformatics tools, as compared to development of bioinformatics tools which is more relevant at the graduate level. Administrative issues encountered are also discussed. This work was supported by a NSF HBCU-UP grant.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.026 |
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".