Weaving Science Communication Training through an Undergraduate Science Program with a Focus on Accessibility and Inclusion
Bibliographic record
Abstract
Science communication training can help scientists engage diverse audiences with the promise and process of science, helping to strengthen science literacy and preserve public trust in science. But not all scientists have access to such training. To address this shortfall, we have embedded a suite of science communication courses in the Life Sciences Program, the largest undergraduate science program at McMaster University in Hamilton, Ontario. A foundational course focuses on making science accessible through inclusive language and media, while more advanced courses emphasize the importance of understanding and centering the values, beliefs, questions, and critiques of audiences, and using narratives and rhetoric to inform, inspire, and ignite change. Throughout the curriculum, students engage with and contribute to the scholarship of science communication. They graduate with skills that serve them in diverse careers. In this article, we outline the structure of our curriculum and detail key components of our science communication courses. We also describe a student-led assessment of our curriculum that highlights strengths and opportunities for improvement. Ultimately, we strive to provide a compelling rationale for teaching science communication at the undergraduate level by sharing a framework of replicable pedagogical practices for engaging large cohorts of students with both the theory and practice of science communication.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".