Canadian Science Meets Parliament: Building relationships between scientists and policymakers
Bibliographic record
Abstract
Abstract The first Science Meets Parliament event in Canada was held in November 2018 in Ottawa, where twenty-eight Tier II Canada Research Chairs (a specific class of Canadian university professor acknowledged by their peers as having the potential to lead in their field) from diverse disciplines met with forty-three Members of Canadian Parliament and Senators. The main goal of this event was to facilitate communication between these two key pillars of the society, to promote mutual understanding of the nature of their respective work, roles, and responsibilities, and to build long-term relationships. Here, we, representatives of the first cohort of scientists to participate in the program, summarize our experiences and lessons learned from this event, as well as our assessment of the benefits of attending this event for scientists, policy decision-makers, and institutions. Furthermore, we provide suggestions for similar future events in Canada and elsewhere.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | high |
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 | 0.071 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.086 | 0.020 |
| Scholarly communication | 0.025 | 0.008 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".