Weaving Science Communication Training through an Undergraduate Science Program with a Focus on Accessibility and Inclusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it