From my casa to yours : evaluating the possibility of applying the Montessori language curriculum to the Ontario elementary context
Bibliographic record
Abstract
The Ontario Human Rights Commission's inquiry into how reading (an essential human right) is taught in Ontario schools revealed that Ontario’s public education system is “failing students...by not using evidence-based approaches to teach them to read.” This thesis aimed to address this failure by proposing Montessori language curriculum as a viable alternative to the current (ineffective) practices. The validity of both the new Ontario language curriculum and the Montessori language curriculum were assessed through the use of The Reading League’s Curriculum Evaluation Guidelines (CEG) and their associated workbook. \n \nThe Ontario language curriculum was found to be lacking, particularly in terms of its lack of clarity, and guidance with regards to practical implementation. The Montessori language curriculum on the other hand was found to meet or surpass the Ontario Human Rights Commission’s recommendations, and in a way that can tangibly be understood and implemented. \n \nIn light of these findings, Ontario school boards should seriously consider adopting Montessori language curriculum, in whole or in part, to address the systemic reading crisis. All educational stakeholders have a role to play in safeguarding children’s right to read.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".