Designing an open course to highlight the work of underrepresented STEM scholars
Bibliographic record
Abstract
Faculty across science, technology, engineering, and math (STEM) disciplines are interested in making their instructional materials more representative of their students' identities.However, they often lack guidance and time for finding these materials.Utilizing our specialized skills in finding resources as librarians, we developed the Representation in STEM (RIS) course to provide resources and guidance on finding and using more representative materials across STEM disciplines.The course is comprised of pages that can be adopted as micro-lessons in disciplinary courses, lowering the barrier for faculty to participate in more inclusive instruction.To ensure RIS is as useful as possible for faculty and students, pages from the disciplines and special topics sections of the course were sent to faculty at Colorado School of Mines and STEM librarians across the United States and Canada for review.We sought feedback on organization, breadth of coverage, and content depth that can be incorporated in the ongoing development of the course.This paper describes the development of RIS, initial feedback received, and lessons learned in the design process that could inform similar projects at other institutions.The glaring gap in guidance for faculty led us to develop the Representation in STEM (RIS) open mini-course.This course was designed to provide faculty with a single page of adaptable content related to representation in a specific discipline or topic area that can be easily used in their disciplinary courses.The full course currently contains five modules with the following content:
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".