THE DEEP RIVER SCIENCE ACADEMY: A UNIQUE AND INNOVATIVE PROGRAM FOR ENGAGING STUDENTS IN SCIENCE
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
For 28 years, the Deep River Science Academy (DRSA) has been offering high school students the opportunity to engage in the excitement and challenge of professional scientific research to help nurture their passion for science and to provide them with the experience and the knowledge to make informed decisions regarding possible future careers in the fields of science, technology, engineering, and mathematics (STEM). The venue for the DRSA program has been a six-week summer science camp where students, working in pairs under the guidance of a university undergraduate tutor, contribute directly to an on-going research program under the supervision of a professional scientist or engineer. This concept has been expanded in recent years to reach students in classrooms year round by engaging students via the internet over a 12-week term in a series of interactive teaching sessions based on an on-going research project. Although the research projects for the summer program are offered primarily from the laboratories of Atomic Energy of Canada Limited at its Chalk River Laboratories site, projects for the year-round program can be based, in principle, in laboratories at universities and other research institutes located anywhere in Canada. This paper will describe the program in more detail using examples illustrating how the students become engaged in the research and the sorts of contributions they have been able to make over the years. The impact of the program on the students and the degree to which the DRSA has been able to meet its objective of encouraging students to choose careers in the fields of STEM and equipping them with the skills and experience to be successful will be assessed based on feedback from the students themselves. Finally, we will examine the program in the context of how well it helps to address the challenges faced by educators today in meeting the demands of students in a world where the internet provides instant access to information.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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