Indigenization of Curriculum in Vinzons District and Its Effects to the Learning Engagement of Dumagat Learners
Bibliographic record
Abstract
The indigenous education system has been facing global challenges and systemic inequities against the IP learners across the globe. The global educational challenges in IP education commonly encompass limited educational access, linguistic barriers and limited curriculum provisions and integration for indigenous knowledge systems and culturally-responsive instruction for IP learners. Global organizations like UNESCO and UNICEF have continue to figure out how colonial evolution and prevalence continue to put indigenous languages and cultures in marginalized status. This marginalization commonly leads to serious educational adversities such as high drop-out rate and increasing illiteracy rate and decline in learning achievements of ethnic group learners. In response to the above cited challenges, global studies also stress the value culturally-relevant and community-based educational programs and services for IP learners. Functional and sustainable education models globally highlight the importance of incorporating indigenous knowledge system, culture and ways of life in the education curriculum towards fostering improvement in the educational outcomes of the IP learners. In fact, there are bilingual systems and intercultural educational programs in countries like New Zealand, Canada and Bolivia have demonstrated the positive implications of the active engagement of IP communities in defining and enriching the educational landscape and systems. Global bodies of research have been continuously highlighting the fact that recognizing the rights of IP learners in education is not only a matter of equality and social justice, but also as integral practices towards cultural enrichment and sustainable growth of communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".