Redefining and developing professional competencies for early childhood education and care
Bibliographic record
Abstract
The competences expected from early childhood education and care professionals have evolved \nsignificantly in our Western countries following the transformation of services and the affirmation of their \neducational function. This article is based on action research projects carried out in the Wallonia Brussels \nFederation (FWB) which integrate an analysis of other education and training systems (France, Flanders, \nEngland, Sweden and Quebec). It presents a reflection on the development of professional competencies \nand their acquisition from the initial training in a holistic approach to education. It notes that a diversity of \nformations coexists, leading to an absence of clear view on professional skills and profiles. In response to \nthis observation, it proposes a modeling that articulates organizational, relational and reflexive \ncompetencies that are needed for the development of a professional posture. That modeling is presented \nas a grid of analysis that can enable better understanding of the complexity of early education and care in \nits many dimensions (working with children, families, professionals and the local community). It can be \nconsidered as a tool to globally rethink initial and continuing education and training in a systemic \nperspective.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".