A Comparative Analysis of Social Studies Curricula for Enhancing Global Citizenship: A Case Study of New York State, the United States, and Thailand
Bibliographic record
Abstract
This research aims to compare the social studies curricula of Thailand and New York State, USA, analyze their similarities and differences, and propose guidelines for improving the Thai social studies curriculum. The study employed a qualitative research methodology, using documentary analysis of the Thai Basic Education Core Curriculum B.E. 2551 (Revised B.E. 2560) in the social studies, religion, and culture learning area, and the New York State K-12 Social Studies Framework. The findings revealed that the social studies curricula of Thailand and New York State differ in many aspects, including their fundamental philosophies, goals for student development, curriculum structures, learning content, and instructional approaches. The Thai curriculum emphasizes cultivating morally good citizens with a love for the nation, while the New York curriculum focuses on developing knowledgeable, skilled citizens who actively participate in a democratic society. In today's rapidly changing world, the development of Thailand's social studies curriculum should foster 21st-century skills, digital citizenship, and a sense of global citizenship among learners while maintaining Thai identity and values. Policy-level recommendations include creating a new vision, designing a flexible curriculum, developing online platforms, and integrating artificial intelligence. At the practical level, suggestions include creating community learning innovations, using the city as a classroom, developing a competency-based curriculum, and building learning communities with local partners.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".