Bibliographic record
Abstract
There is a reading crisis in the U.S. where many teachers do not know how to teach children to read. “You learn to read from kinder to 3rd grade, so that you can read to learn from 4th grade on.” Unfortunately sixty five percent (65%) of all US fourth graders cannot read at grade level. (The Annie E. Casey Foundation: National KIDS COUNT. 2015). Research makes it clear that most children require direct instruction in order to learn to read. (“National Reading Panel” Chapters 2 & 3. 2000) Over a period of 40 years, the writer has prepared more than 2,500 Montessori Teachers in Canada, U.S.A., Mexico, Costa Rica, Panama, Ecuador, Brazil, France and Switzerland. His alumni have opened 25 Montessori schools in Costa Rica and more than 100 Montessori schools in Mexico. The author and his alumni have used the teaching strategies described in this monograph to teach thousands of children to write and read successfully. This study also challenges the Conventional Wisdom that “books are in print so we must teach children to print.” The writer’s research demonstrates that Conventional Wisdom is wrong and he challenges the reader to consider the benefits of teaching children to master longhand cursive writing instead of print, because it facilitates both the process and the quality of writing and reading.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.069 | 0.062 |
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".