THE DEEP RIVER SCIENCE ACADEMY: A UNIQUE AND INNOVATIVE PROGRAM FOR ENGAGING STUDENTS IN SCIENCE
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".