Communicating Ocean Sciences to Informal Audiences: A Scientist-Educator Partnership to Prepare the Next Generation of Scientists
Bibliographic record
Abstract
Communicating Ocean Sciences to Informal Audiences (COSIA) is a college course that creates and develops partnerships between science educators in informal science education institutions, such as museums, science centers and aquariums, and ocean scientists in colleges and universities. For the course, a scientist and educator team-teach inquiry-based science pedagogy and communication strategies to graduate and undergraduate students in marine science-related majors. In addition, students participate in practicum experiences in informal science education institutions. COSIA aims to engage the next generation of scientists (college students) in learning to communicate their scientific knowledge with the general public. This paper describes how this course and the resulting partnership address some major issues in scientific literacy, as well as how they benefit all the stakeholders (scientists, science educators, college students, and visitors to informal science institutions). Three central challenges to creating partnerships and how they were addressed are outlined, and several underlying principles for initiating and growing partnerships between individuals and institutions are presented. Through the process of engagement in this effort and the development of partnerships between educators' and scientists' communities of practice, it is argued that a new hybrid community of practice may be emerging.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".