Comparing Successful Education Approaches From the Global South and Canada
Bibliographic record
Abstract
Educational reforms have been enacted to strategically address critical issues facing schools at the local (individual classroom, school, district) or systemic (provincial and national) level. At the systemic level, large educational change movements often present difficulties in the areas of sustainable success (Fullan, 2021). Across the globe, nations’ economic success is strongly correlated to students’ mathematics performance (Farrell et al., 2017). Therefore, the education of school-aged children in mathematics is an area of concern in both the Global North and Global South. Although many Canadian initiatives have addressed areas of improvement for mathematics education in schools over the last 10 years, limited research and few such initiatives have considered success stories beyond the Global North, thus overlooking new radical approaches for the core subjects, including mathematics (Farrell et al., 2017). This major research project conducted a comprehensive literature review exploring three Canadian mathematics education initiatives—Building Thinking Classrooms (Liljedahl, 2020), Math Minds (Davis et al., 2020), and Show Me Your Math (Lunney Borden, 2010)—as well as three alternatives from the Global South: Escuela Nueva (Colbert & Arboleda, 2016), Learning Community Project (Rincón-Gallardo, 2019), and Bangladesh Rural Advancement Committee Non-Formal Primary Education (Numan & Islam, 2021). Findings from this study can stimulate a flow of ideas between the Global South and Global North, with a detailed comparison between the Canadian and Global South approaches that can enhance mathematics education.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".