Hidden Curriculum in Schools: A Comparison of Religious Otherness in Pakistani ELT Textbooks
Bibliographic record
Abstract
The emphasis of this research was religious otherness depiction in high school English textbooks issued by four State controlled Pakistani textbook boards of i.e., BTB Quetta, STB Jamshoro, KTB Peshawar and PTB Lahore. It besets a broad contrast among the religious otherness descriptions as depicted in provincial ELT textbooks and the otherness related notions of their corresponding students. To achieve the goals, the study was alienated in 2 phases: In phase 1, the textbooks of government textbook publishing boards were investigated and in the second phase their corresponding readers’ religious otherness ideas were obtained and evaluated. The research devised a modified model of analysis by blending Van Dijk (1998) and Fairclough (2001) CDA model for interpretation and explanation of religious otherness in representative text extracted from textbooks’ discourse. The study discovered the prevalence of religious otherness- related themes in all ELT textbooks. It was also found that STB discourse had improved religious otherness images and students’ otherness ideas than other provincial textbook boards and their respective readers. The study also revealed that Muslim male and female students had peculiar otherness notions about minority religious communities. The readers’ responses to questionnaire items in phase 2 of research suggested that textbooks had a significant part in molding otherness related notions of young readers. The study recommended an otherness-based investigation of the textbooks prior to publication at federal government level to ensure citizenship equality as envisioned by founder of the nation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".