A Data Mining Approach to Comparing American and Canadian Grade 10 Students’ PISA Science Test Performance
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
According to 2006 Programme for International Student Assess ment (PISA), sixteen Organization for Economic Cooperation and Develop ment (OECD) countries had scores that were significantly higher than the US. The top three performers were Finland, Canada, and Japan. While Finland and Japan are vastly different from the US in terms of cultures and educational systems, the US and Canada are similar to each other in many aspects, thus their performance gap was investigated. In this study data mining was employed to identify factors regarding access to and use of resources, as well as student views on science for predicting PISA science scores among Grade 10 American and Canadian students. It was found that science enjoyment and frequent use of educational software play important roles in the academic achievement of Canadian students.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.014 | 0.006 |
| 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