Clarifying the Effective Factors of Hidden Curriculum of Schools on Establishment of the Aims of Religious Education of Elementary School Students (Case Study of Ahvaz City)
Bibliographic record
Abstract
Current research aims to clarify the effective factors of hidden curriculum of schools o the establishment of the general goals of religious education in elementary period.This research is practical in regard to classification of the researches on the base of purpose and approaches the issue in phenomenological manner. Statistical population of the research includes all the principals, teachers and boy students of fifth and sixth grades of elementary schools in Ahvaz city and the samples of the research are selected through purposive sampling.Data are gathered through unstructured interview conducted by means of open answer questions and have been continued as much as saturation of the data.Grounded theory procedure or theory derived from data is used to analyze the data and to achieve intended model (theory) of the research and it includes four stages: 1-Coding 2-conceptualizing 3-Categorizing 4-Compiling the theory (model) of findings : In the next stage, 15 concepts of these codes are extracted and in the third stage, the mentioned concepts are organized in form of 5 categories(factor) : cognitive environment , social environment, physical environment, administrative environment, religious environment, and in the last stage, research theory (model) is formed on the base of discovered categories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".