Bibliographic record
Abstract
As the results of PISA2006 (OECD, 2007) and other international assessments have been realized, there are concerns in Japan that although students show the high score of scientific literacy their interests in science were low. In this study, we explore the structure of attitudes towards science and scientific literacy based on the comparison of following five countries: Australia, Canada, Finland, Japan and New Zealand. The model of this analysis includes the six constructs (f 1:ENJ, f 2:GEV, f 3:PEV, f 4:FUM, f 5:GEI, f 6:INM) of attitudes towards science and scientific literacy score (f 7:SCL) with multiple group method. As the results of this analysis, the direct effects of each construct and total effects on f 7:SCL show the similarity between Australia, Canada and New Zealand. On the other hand, the total effect of f 5:GEI on f 7:SCL in Japan is bigger than other countries' one.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".