Bibliographic record
Abstract
Students in Finland had the highest science scores in a 57‐country survey of 15‐year‐olds. According to a 4 December report issued by the Organisation for Economic Co‐operation and Development (OECD), other high‐scoring countries included Canada, Japan, New Zealand, Hong Kong‐ China, Chinese Taipei, and Estonia. More than 400,000 students from 57 countries participated in the Programme for International Student Assessment (PISA), a triennial survey of the knowledge and skills of 15‐year‐olds. The study found that on average across OECD countries, 1.3% of 15‐year‐olds reached level 6, the highest proficiency level on the science scale. These students could consistently identify, explain, and apply scientific knowledge in many situations. The number of students at that level could not be reliably predicted from a country's overall performance. For instance, while Korea had an overall high score of 522 points (above the average 500) and the United States had a score of 489, both countries had similar percentages of students at level 6. For more information, visit the Website: http://www.oecd.org.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".