Global Diffusion of Montessori Schools
Bibliographic record
Abstract
Montessori education is distinct for its implementation in 154 countries around the world. Lacking a Montessori trademark or comprehensive overseeing body, the expansion of the Montessori approach has often been diffuse and fragmented among competing organizations. The absence of centralized, accurate, and consistent accounting has made it difficult to document the scope, growth trends, and diverse populations of students served in Montessori schools. The primary objective of this study was to gather evidence to support a robust estimate of the number of Montessori schools worldwide. This estimate relies on national and regional organizations’ broadest definitions of what constitutes a Montessori program. The study included two components: a survey of regional and national Montessori organizations and supplemental sources, including other published estimates and direct inquiries within key countries. Multiple sources allowed for triangulating data to reach a more confident estimate for the number of schools in each country and for synthesizing global perspectives on significant elements of Montessori fidelity worldwide. Through these sources, we document a total of 15,763 Montessori schools around the globe, roughly 9% of which are government funded. Countries with the largest number of Montessori schools are the United States, China, Thailand, Germany, Canada, and Tanzania; the United States, Thailand, the Netherlands, and India have the largest number of government-funded or public Montessori programs. Results of the fidelity analysis identified six practices that emerge consistently as central pillars of Montessori implementation.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".