Medias and Electronic Medias as one of the Factors affecting Pre-School Education
Bibliographic record
Abstract
Pre-school education is a very important step in preparing children for primary school by developing their linguistic, kinetic, sensory, emotional and social skills. As long as education is a social system and dynamic process aims at shaping human behavior so that it conforms to dominant patterns of social organization, it is also subject to various influences arising from the social change phenomenon which is the most important technological factor, for its impact on the various systems and social patterns, including the cultural and educational factor. The electronic media and mass media are one of the information revolution products narrated by the human societies in the last quarter of the twentieth century, which brought about deep structural transformations in socialization methods and particularly in educational methods, where the media has become a competitor to traditional methods of education, including pre-school. Thus, the child is now living in the world of power of image, sound and speech, influenced by the programs which may affect their behavior and perception of the world, as well as their intellectual growth and sense of movement, which creates a discrepancy between what he acquires from media and what he receives in the pre-school, which is supposed to be preparatory for the subsequent school stage. From these points of view, we wanted to approach the subject from a sociological-educational perspective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".