Visiting Mother Tree schools in India: Creating Conditions of Enablement
Bibliographic record
Abstract
Most nations globally who are working to build quality educational systems are also struggling with the education of students who learn differently from the mainstream school population. Recently we visited five schools in India who have taken extraordinary steps toward building enabling environments that in-clude all students. While analyzing our conversations with these remarkable schools, we construct a meta-phor of the Mother Tree School. We cite research on mother trees indicating that trees connect to each other through fungi root connections underground. This allows trees to communicate needs to their network, and the mother trees transfer nutrients silently and unobtrusively to the trees that need it most. Remarkably, the leadership of the mother tree allows for specialized support yet also enables all of the trees to flourish. Similarly, we find that some schools in India, even prior to the Right to Education Act that came into effect in 2010, are providing unique examples of teachers and parents working within communities of practice to provide active, experiential learning expe-riences that show student-centered pedagogies. The Mother Tree schools' unique blend of pedagogy and professional development allows them to meet the needs of more students, including those with excep-tional learning profiles.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".