When thousands of citizens innovate: how policy-makers can contribute
Bibliographic record
Abstract
The COVID-19 pandemic is a great challenge to our global society, exposing our limitations as well as new ways to generate adequate responses to global crises. Communities and individuals have spontaneously organized to deal with this crisis. Thousands of skillful individuals have engaged in the development of mechanical ventilators and masks, SARS-CoV-2 test kits, mobile applications for contact tracking and for coordinating mutual help and care, to name just a few. [...] we advise that the Canadian Government recognize this movement. This would lead to a second step of creating a normative system to regulate this new sector and to legitimize it [...]. _________ Featured Editorial Series "Response to COVID-19 Pandemic and its Impacts" by the Canadian Science Policy Center. Access all editorials.
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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 | 0.056 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.047 | 0.031 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.036 | 0.026 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".