Canadian Science Meets Parliament: Building relationships between scientists and policymakers
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 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 | 0.044 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.074 | 0.340 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.002 |
| 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