Beyond Punnett squares: Revising the Canadian genetics curriculum to meaningfully introduce social and ethical perspectives
Bibliographic record
Abstract
In 2015, the Genetics Society of America published a learning framework based on Vision and Change. One of many core competencies was that “Students should be able to identify and critique scientific issues relating to society or ethics.” Unfortunately, no links to other core concepts were provided to help instructors integrate this core competency into the genetics curriculum, and no key issues were identified or discussed. Traditional genetics concepts raise many unresolved ethical issues (Gouvea, 2022; Visintainer, 2022) and new genome-editing technologies amplify the need to incorporate social and ethical perspectives in the genetics curriculum. A few exercises that touch on social and ethical perspectives in genetics have been made available through CourseSource, but these are mostly superficial and difficult to integrate into the existing curriculum. Moreover, little work has been published on the curriculum for Canadian introductory genetics courses in higher education. To address this deficiency, we conducted a document analysis of syllabi for introductory genetics courses across all provinces and territories. Only 15% of these syllabi touch on social and ethical perspectives. Through interviews of students and instructors in courses across Canada, we are exploring how these perspectives have been integrated into the curriculum. We will share the themes emerging through our interviews, and invite attendees to consider alignment with their own pedagogical contexts. We will share our work to build a database of strategies to integrate social and ethical perspectives in introductory genetics courses across Canada. This study was performed with ethics approval.
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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".