Pre-school education close to natural environment: Studying Parameters on Parental Choice and Dedication
Bibliographic record
Abstract
Preschool is a period of rapid development of skills and learning, the infant learns through family, school and especially through exploring the natural environment. The child, playing in an outdoor environment close to natural environment, naturally develops his talents, his creativity but also many motor skills, which otherwise, indoor learning environments would not give so many opportunities. Outdoor learning environments in natural environments contribute significantly to the learning of children, especially young children. This paper explains the importance of designing learning environments close to natural environment, something that parents have realized and now consciously choose such environments for their children's learning and development. This article examines the parameters involved, in detail which of the 7ps' elements of Educational Marketing most influence the choice of parents with children in East Thessaloniki to make decisions about their children attending Private Kindergartens with large yards, away from the urban environment and in areas close to natural environment and natural areas. The results of the research show that these parents are of a high educational level, graduates of a University or Technological Institute, with a Master's degree or Doctorate. Also, when parents are satisfied with the quality of the school space, the existence of pedagogical materials and toys for the employment of children, the existence of an outdoor large yard and outdoor activities with infrastructure and facilities away from the urban environment and close to natural environment then they choose this Pre-school education for their child's attendance, they recommend it voluntarily to acquaintances and friends and would choose their child to continue attending to a next level of education such as Primary School if there was one.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".