Achievement of Cognitive Development Aspects of Children Aged 5-6 During the Covid-19 Pandemic at Santa Sesilia Kindergarten, East Flores Regency
Bibliographic record
Abstract
This study aims to describe the Achievements of Cognitive Development Aspects of Children Aged 5-6 Years before the Covid-19 Pandemic, and the Achievements of Children's Cognitive Development during the Covid-19 Pandemic, at Santa Sesilia Kindergarten, Lewokluok Village, Demon Pagong District, east Flores Regency. The method used in this study is a qualitative approach. Interview data collection techniques and documentation studies. The data analysis techniques used are data reduction, data presentation and drawing conclusions. The results showed that the achievement of aspects of children's cognitive development before the covid-19 pandemic, most of their children were in the category of developing very well and developing as expected, and the achievement of aspects of children's cognitive development during the covid-19 pandemic showed that most children were in the category of undeveloped and began to develop. The conclusion of this study is that the achievement of aspects of cognitive development of children who were in the school year during the covid-19 pandemic decreased because most of their children were in the category of undeveloped and began to develop, compared to children who were in the school year before the covid-19 pandemic, most of whose children were in the category of developing as expected and developing very well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".