Lessons in LeArning Informal science learning in Canada
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
“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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it