Analysis of negative expressions in the 2022 Revised Mathematics Curriculum: A comparative study with the U.S., U.K., Canada, Singapore, and Australia
Bibliographic record
Abstract
This study aims to analyze the instances of negative expressions found in the 2022 Revised Mathematics Curriculum documents and to explore their implications for mathematics education. To this end, a qualitative content analysis was con ducted, and comparisons were made with mathematics curriculum documents from the United States, the United Kingdom, Canada, Singapore, and Australia. The findings revealed that the 2022 Revised Mathematics Curriculum contained a high fre quency of negative expressions, whereas the curriculum documents of the comparison countries were primarily framed using positive expressions, with negative expressions appearing only in a very limited manner. In particular, the most frequently occur ring negative expression in the 2022 Revised Mathematics Curriculum, “should not covered,” was found to potentially constrain teachers’ instructional practices and restrict students’ opportunities for exploratory learning. Based on these findings, this study suggests that not only mathematics curriculum documents but also mathematics education policy more broadly should shift from negative expressions toward positive and open-ended language, thereby ensuring teachers’ autonomy and supporting students’ opportunities for inquiry.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".