Bibliographic record
Abstract
In Ontario, Canada, early childhood educators are experiencing the beginning of what could be a radical democratic shift in pedagogical thinking and approach for the early years (birth to 6 years). This shift in pedagogy, articulated in the Ontario Ministry of Education’s 2014 pedagogy document ‘How Does Learning Happen?’ (HDLH), acknowledges children, and the adults who live and work alongside them, as curious, capable, and competent creators of culture in democratic society. While this shift has enormous possibilities, the chapter reveals the existing fragmentation between the views articulated in HDLH and the various entrenched organizational and governance structures, policies, and pedagogical practices in Ontario’s early years educational system. In reviewing the situation, the authors draw on the dynamic languages of Complexity sciences and theoretical frameworks to examine the systems active in Reggio Emilia in a search for underlying patterns that might illuminate the challenges in Ontario as attempts are made to enact an early years system that truly embraces a view of children, families, and educators as portrayed in HDLH.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".