Nature-based early childhood education, a fertile ground for instructional support in Quebec early childhood centers? Yes, but...
Bibliographic record
Abstract
The purpose is to investigate the Instructional Support (IS) offered by early childhood educators who work in nature-based education (NBE; e.g., woodland, field). The quality level of IS tends to be low, so much so that it does not provide a significant influence on young children's learning (Burchinal et al., 2010, 2016). However, NBE could provide fertile ground for the quality of IS \nin young children, because of the principles that frame it (e.g. loose parts, child-centered approach). The Teaching through Interactions model (Hamre et al., 2013) underlines the conception of IS. As part of a pragmatic paradigm and by using an embedded esearch design, the quality of IS (scores) was observed in 20 groups of 3-5 years-old children from 8 early childhood centers in Quebec (Canada), with the CLASS (Pianta et al., 2008). Afterwards, a semi-structured interview was also conducted with the educators of these groups. Their words were analyzed considering the indicators of the IS (e.g., open-ended questions). All participants (educators and parents) signed a consent form. The children gave their verbal consent to their educator before proceeding with the observations. Preliminary results indicate that 6/20 of the educators stand out with the IS score that is above the recommended threshold of 3,25/7. Their comments shed light on how NBE is conducive to IS for early childhood centers. These results will be discussed in relation to the contribution of the pedagogical principles of nature-based education to IS, for the purpose \nof orienting policies in education, public health, etc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".