Metodologías de investigación incluyentes y esfuerzos comunitarios en la revitalización del idioma náhuatl
Bibliographic record
Abstract
This research paper will stimulate intercultural debate around indigenous epistemologies of the Global South. It also intends to raise awareness about the importance of indigenous languages for social development and pedagogical practices. This research is based on inclusive research methodologies with native speakers of the Nahuatl language during four fieldwork seasons between May 2017 and March 2019, inside different indigenous communities such as San Miguel Xaltipan and San Pedro Tlalcuapan in Tlaxcala, but also Santa Ana Tlacotenco in Mexico City. This research uses mixed methods (quantitative and qualitative), and one of its goals is to achieve a deeper insight into the interplay between sociolinguistics and philosophy. The data gathered are based on conversational methodologies and extended interviews with indigenous scholars, teachers, and native speakers devoted to promoting capacity building and preservation of language, traditional knowledge, and cultural heritage. Most Nahua scholars interviewed headedor collaborate with a grass-roots organization that works independently and regularly without government support or funding. Also, the academic research of Nahua scholars has been instrumental in figuring out the framework of this research. This research is based on direct translations of unpublished material from Nahuatl to English. Inclusive research methodologies help face the question: why is it important to encourage, study, and promote indigenous languages globally?
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".