STEMxPolicy: An approach to empowering STEM students to become leaders in policymaking
Bibliographic record
Abstract
Public policy is informed by many stakeholders and bodies of knowledge. As policies are needed for topics rooted in STEM such as climate change, AI, health, cybersecurity, etc., it is vital that knowledge translation between researchers and policymakers is prompt and efficient. The current 1–2-decade gap in knowledge acquisition and implementation is insufficient (Curran et al., 2011). Researchers and policymakers should not be two mutually exclusive groups. STEMxPolicy is a new student organization in the Faculty of Science’s USci Network based in student experiential learning and research. Our goal is to bridge the existing gap between STEM and policy by educating students about STEM policy, engaging them in policy-based discussions, and empowering them to become leaders in policymaking. Over the past year, we have hosted two panels and three “Snapshot Seminars” featuring expert panelists that have sparked thoughtful dialogue. The seminars have provided students an overview of the relevance of policy-making within STEM, field-specific considerations in constructing policy, and guided students to get involved. Social media has also been leveraged to circulate educational “Vlog Interviews” and “Hot Topic” posts and promote virtual events. Our work has created numerous student-faculty partnerships and engagement opportunities with high-profile speakers from large provincial and national organizations such as the Ontario Chamber of Commerce, Registered Nurses Association of Ontario, and National Research Council. We anticipate that this model of connecting STEM and policy will translate into more STEM professionals being at the forefront of the policymaking process.\nCurran, J. A., Grimshaw, J. M., Hayden, J. A., & Campbell, B. (2011). Knowledge translation research: The science of moving research into policy and practice. Journal of Continuing Education in the Health Professions, 31(3), 174–180. https://doi.org/10.1002/chp.20124
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".