Content Analysis of Textbooks of Social and Pakistan Studies for Religious Tolerance in Pakistan
Bibliographic record
Abstract
This study was conducted with the objectives of identifying the content that promotes religious tolerance, intolerance and strength/weakness of concept of religious tolerance in Social Studies and Pakistan Studies textbooks. This study was delimited to the content analysis on religious tolerance and intolerance of Social Studies textbook of 8 the Class and Pak Studies textbooks of 10 the class published by the NWFP textbook board Peshawar, Pakistan. In the light of the curriculum draft, the content of both subjects of both classes was analyzed in the light of using qualitative methods. In the curriculum draft, three objectives on religious tolerance for Social Studies (Enhance Sympathy for other people, Love for humanity, and Service for mankind) and two objectives (Lay emphasis on the right and obligation of the citizen of an independent and sovereign state, and Inculcate awareness about the multicultural heritage of Pakistan so as to enable the students to better appreciate the social cultural diversity of Pakistani society and used to with the idea of unity of diversity in our national context) for Pakistan Studies were found. The Social Studies objectives were translated only in one lesson and the two objectives of the Pakistan Studies were translated only in three lessons in the text books. There were some supportive material on tolerance and intolerance. It is recommended that the intolerance and hate supportive material should be excluded and more tolerance supportive material should be included. The religious tolerance material should be propagated through educational institutions and media. Further research is also recommended for the content analysis of other textbooks at every level of education in Pakistan.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| 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) | 0.005 | 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".