Academic Activities after School That Help Secondary School Children’s Cognitive Development through Hermeneutic Analysis
Bibliographic record
Abstract
This research is an effort to look into academic activities after school that help secondary school students in cognition development through Hermeneutic analyse for students of Pantai Remis Secondary School, Perak. This research is also to show that Hermeneutic understanding method can be applied effectively to identify academic activities after school that help secondary school students in their cognition development. This research involved 20 secondary school students from Form 1 to Form 3. They were asked to write a reflective essay about academic activities that they did after school hours. Their reflective essays were made a research text that will be using Hermeneutic analyse to find out academic activities that help their cognition development. This research shows that if a student can do positive activities after school such as doing revision, going for tuition, attending classes, doing homework, studying at home, doing module paper, involving school work, carrying out exercises, attending extra classes, involving in ritual study, going to class and attending study with teacher, involving in preparation class, doing mathematic exercises, reading, doing notes, going to library to study, reading geography, reading notes from internet, watching Astro channel that shows education programmes and reading books, thus they can help their cognition development and become excellent students in academic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".