Whose Job is it Anyways? A Study of Attitudes and Perspectives among Canadian Science Communicators with a Comparison to Global Practices
Bibliographic record
Abstract
Science communication is essential in sharing and discussing scientific research with people outside of specialized scientific audiences. Diversity among science communicators, researchers, and audiences is evident on both a local and global scale, necessitating training programs that serve to enhance communication skills, thereby improving overall communication effectiveness. This study seeks to enhance our understanding of science communication in Canada, including barriers and challenges the field currently faces. An online questionnaire was conducted to compare attitudes and approaches of Canadian communicators from various backgrounds, fields, and disciplines including scientists, journalists, podcasters, online content creators, and artists. Most Canadian science communicators had a background in science (rather than journalism or communication), had limited training in communications prior to starting their careers, and indicated that scientists in particular should be trained in science communication. Diversity was listed as both a positive aspect of Canadian science communication, and a challenge needing to be overcome (specifically that diversity is still lacking). The findings suggest that greater emphasis on communication training is needed, especially for young, early career scientists, and that equity, diversity, and inclusion were important. Gaps in knowledge were identified regarding the accessibility of science communication, as well as the impact of older practices such as Indigenous oral histories. A better understanding of the Canadian science communication landscape can help to design and enhance training, support, and outreach initiatives, for improved public engagement with science across the country.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".