First Nations Education and Inclusion in The Philippines: A Study of Textbooks
Bibliographic record
Abstract
Education is a fundamental right of every person. Yet many First Nations groups around the world continue to be excluded from educational systems that do not recognise their cultures. Despite educational reform in The Philippines through the early 21st century, the impact of a long colonial history continues to influence the education of First Nations students. This research investigates how the Philippine education system accommodates First Nations students. It examines educational policy aimed at including First Nations students in education. To address these aims, the study employed critical discourse analysis and Nancy Fraser’s social justice theory to examine power relations that shape knowledge distribution in education in The Philippines. In this study education policy was investigated and compulsory school textbooks were analysed. The research focused on the intent of the Enhanced Basic Education Act of 2013, and its implementation. Social studies textbooks used in primary education in public and private schools were also analysed. Finally, the findings from the analysis of literature, educational policy, textbooks and Philippine colonial history were synthesised. This research reveals that power inequality and the embeddedness of the colonial ethos, including the colonial discourse of civilising missions, determine how First Nations students are accommodated in Philippine education.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".