The move: Reggio Emilia‐inspired teaching
Bibliographic record
Abstract
In the Rainbow District School Board, we have been refining our approach to Early Learning and inquiry learning in the primary grades for the past five years.Our work in Early Learning is primarily inspired by the preschools of Reggio Emilia, a city in Northern Italy about the same size as the city we live in.The preschools in Reggio Emilia gained international recognition for being the best in the world, beginning in 1991 when they were cited in Newsweek magazine as one of the ʺbest top ten schools in the world."They have consistently won awards and recognition since.For 70 years the Reggio educators have studied how young children learn, refining their theory of learning and teaching.The Reggio Emilia perspective shifts the focus of the classroom away from the teacher and onto the students, viewing children as capable, self-reliant, intelligent, curious, and creative.This approach also treats the classroom as the 'third teacher', encouraging teachers to take a great deal of care in the creation and setup of the environment of the classroom and the materials that are introduced.Finally, this approach positions the teacher as a researcher, documenting the children's relationships and interactions with people, ideas and materials in the classroom.In the Rainbow District School Board, our understanding of the Reggio Emilia approach is ever evolving.Each year we have decided on a new focus to help grow our understanding of the elements involved in this type of approach.Some things we have focused on in the past are: treating the outdoors as an extension of the classroom, using the arts as a vehicle for learning and teaching, documentation as assessment, the
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".