Lessons in LeArning Informal science learning in Canada
Bibliographic record
Abstract
“Without a scientifically literate population, the outlook for a better world is not promising.” —Art Hobson Science is playing a growing role in public policy and in the daily lives of most citizens. As a result, science literacy skills are becoming increasingly important. A scientifically literate person understands basic scientific concepts, is aware of the strengths and limitations of the contemporary practice of science, and can access and evaluate science-related information. Governments and individuals alike must grapple with complex issues such as global warming and stem cell research. Consumers must try to make sense of the allegedly scientific claims of advertisers, and patients face a dizzying array of treatment options. In short, most people confront science-based issues on a regular basis, and scientifically literate individuals are better equipped to engage with these issues and make important decisions that affect their health, security and economic well-being.1 By international standards, Canadian schools are doing an exceptionally good job of teaching science to Canadian youth. On the science portion of the most recent Programme for International Student Assessment (PISA) examinations, 15-year-old Canadians scored well above the average scores for the 41 developed countries that participated in the testing—and were outperformed
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".