Young Voices: The Challenges and Opportunities That Arise in Early Childhood Environmental Education Research.
Bibliographic record
Abstract
The number of early childhood environmental education programs are on the rise in Canada and although young children have been quite marginalized from environmental education research, better understanding young children’s relationship with the natural world is increasingly seen as important. Including young children themselves in research is important; however it is essential for researchers to utilize developmentally appropriate research tools and data collection methods, just as environmental educators should create programs which are congruent with the emotional, physical, and cognitive abilities of young children. Including young children in environmental education research can be a challenge, but there are several data collection methods which have proven to be successful, for example, the Mosaic approach which is discussed in this article.
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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 | 0.193 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.027 | 0.038 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".