The Security Education Concepts in the Textbooks of the National and Civic Education of the Primary Stage in Jordan—An Analytical Study
Bibliographic record
Abstract
The present study aimed at exploring the concepts of the security education in the textbooks of the national and civic education of the higher primary stage in Jordan. It adopted the descriptive analytical method. The study sample consisted of the textbooks of the national and civic education for the basic eighth, ninth and tenth grades. To achieve the objective of the study, a form was prepared for the analysis of these textbooks which contained the security education concepts; (34) security concepts distributed over four areas: the intellectual security, the political security, the social security, and the economic security. The findings of the study showed that the textbook of the national and civic education of the tenth primary grade was of more inclusion of the concepts of security education than those of the eighth and ninth grades. Also, it was revealed that the extent of inclusion of the security education concepts in the textbooks of the national and civic education varies in the higher primary stage, while the level of sequence of these concepts included in these textbooks is low. Additionally, the findings showed that there were no statistically indicative differences in the level of integration of the security education concepts between the textbooks of national and civic education in higher primary stage in Jordan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".