Empowering Practitioners to Critically Examine Their Current Practice
Bibliographic record
Abstract
\n\t\t\t\t\tA Self-Assessment Manual (SAM) for early childhood practitioners has been developed around the previously reported framework (Raban, Waniganayake, Deans, Brown & Reynolds, 2003a), and this has been piloted in a number of early childhood settings. When engaging in self reflection using SAM, practitioners collaborate with mentors from the Early Childhood Consortium Victoria (ECCV) to consider their past, present and future professional experiences. This process empowers practitioners to critically examine their current practice and plan for their future professional development in a more systematic way. SAM is seen here to serve dual purposes: on the one hand, it is an evaluative instrument that can direct professional development of individual practitioners. On the other hand, it is a research tool aimed at identifying theoretical assumptions underpinning professional practice, and allows a longitudinal approach to mapping professional growth and development. Either way, it has the potential to enhance the quality of early childhood experiences for preschool children regardless of the settings in which they may find themselves. This paper presents a formative evaluation of this work and discusses its potential for professional development planning. \n\t\t\t\t
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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 | 0.045 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".