Let’s Create a Harmonious and Peaceful World through Quality Bilingual Education! Indigenous Tsotsil Children and Their Languages the Solution!
Bibliographic record
Abstract
The purpose of this paper is to underline the implications that language endangerment has, not only for the speakers of a specific language, but for the entire world as losing a language involves the disappearance of cultural, spiritual and intellectual knowledge as well as cultural identity. Many indigenous languages in Mexico, for example, have been in danger as Spanish, the dominant language of the country, has put them at a disadvantage. Transitional bilingual education has been used to achieve such a goal. Since this has been the case, some indigenous communities have taken the initiative to work diligently to preserve and promote their native language and culture despite the sociopolitical, economic and educational pressures they face. An example of that is the Mayan Tsotsil community in Chiapas in southern Mexico. This paper offers a summary of the findings of the qualitative research study that was conducted to explore the situation of the Tsotsil language at a Spanish-Indigenous Tsotsil elementary bilingual school in Chiapas. Tsotsil children and their teacher show that it is possible to preserve and promote the Tsotsil language when working together as a community. It is concluded that quality bilingual education and inclusive schools can be great tools that can contribute to have a harmonious and peaceful world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".