Rethinking the Course Syllabus: Considerations for Promoting Equity, Diversity, and Inclusion
Bibliographic record
Abstract
Introduction: Equity, diversity, and inclusion (EDI) are receiving considerable attention in higher education. Within psychology, the American Psychological Association has highlighted the importance of cultural diversity in both undergraduate and graduate curricula and charged educators with facilitating the development of cultural competence among learners. Statement of the Problem: Many resources have been developed to help promote EDI within higher education. The resources developed have mainly focused on the curricula and pedagogical approaches, yet the syllabus remains overlooked with few guidelines available to educators. Literature Review: We offer several considerations informed by theoretical frameworks and best practices in the discipline and suggestions for the successful implementation of EDI in the syllabus. Teaching Implications: This article provides a comprehensive and useful guide for developing a syllabus that assists with the integration of EDI, as the syllabus is the first opportunity for faculty to communicate their philosophy, expectations, requirements, and other course information. Conclusion: Infusing EDI in the syllabus is essential for promoting an inclusive learning environment and is conducive to establishing goals related to cultural competence.
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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 | 0.031 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".