Early Childhood Imagineering: A Model for Developing Digital Storytelling
Bibliographic record
Abstract
The objectives of this research were 1) to synthesize the model of early childhood Imagineering for developing digital storytelling; 2) to establish the model of early childhood Imagineering for developing digital storytelling; 3) to evaluate the model of early childhood Imagineering for developing digital storytelling. The research methodology was divided into three phases: 1) the documents in the Imagineering process, including the LA-OR model process and the stop motion animation process, were synthesized to establish the model of early childhood Imagineering for developing digital storytelling; 2) a model of early childhood Imagineering was established in order to develop digital storytelling; 3) the suitability of the model of early childhood Imagineering for developing digital storytelling was evaluated. The results showed that the model of early childhood Imagineering for developing digital storytelling consisted of the four following steps: 1) L: Leading to Learn consisted of imagination; 2) A: Active Learning consisted of two factors: Designing and Development; 3) O: Opinion Sharing consisted of Presentation; and 4) R: Reflective Thinking consisted of two factors: Improvement and Evaluation. According to an evaluation from experts, it was shown that the experts strongly agreed on the development model of early childhood Imagineering for developing digital storytelling.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".