CELEBRATE Feeding: A Responsive Approach to Food and Feeding in Early Learning Settings
Bibliographic record
Abstract
Early learning and child care (ELCC) settings in Canada follow nutrition standards that outline food provisions, with many also encouraging responsive feeding practices that help to create a supportive environment for children. Caregivers who lack confidence in children's ability to regulate their own intake, or those who feel stressed about mealtime, may unknowingly engage in less responsive feeding practices. The CELEBRATE Feeding Approach is a flexible framework, driven by behaviour change theory, that builds on previous definitions and concepts of responsive feeding in ELCC environments. Through this approach, there is an intentional focus on supporting early childhood educators to implement feeding practices that are more responsive. The approach incorporates 13 target educator behaviours related to the three overlapping categories of CELEBRATE language, CELEBRATE Mealtime, and CELEBRATE Play. These practices recognize and support the development of a child's sense of autonomy, confidence, and self-regulation not only at mealtimes but also through play-based exploration and language that is used throughout the day around food and feeding. The goal is that children will be open to a wide variety of food, develop their self-regulation skills, and build the foundation for a positive relationship with food throughout their lifetime.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".