Diseño de una plataforma virtual enfocada en la preservación de la lengua indígena Otomí
Bibliographic record
Abstract
As a consequence the university was a bit far away I had to make the decision to rent, it was a difficult decision because I was used to living with my parents, during this period I was withdrawing my family and I was about to leave the university but my Mom motive me not to do it.Derived that I would lose a great opportunity in my life that possible would not forgive me. After Indigenous languages are of great importance for a country since it represents their cultural identity being an invaluable heritage. The reasons for preserving the native languages are many, mainly the transmission of culture, but also of social integration and education. Currently in Mexico is among the 10 countries with greater cultural and linguistic wealth and second in the American continent with more indigenous leagues. According to INEGI in 2015, 6.5% of the population in Mexico speaks some indigenous language, (INEGI, 2016) which represents 7 million 382 thousand 785 people of 3 years and more speak some indigenous language, the most spoken are : Nahuatl, Maya and Tseltal (INEGI, 2015). But despite this in recent years has been inevitable language loss and for various reasons whether cultural, social or political. The area historically occupied by the Otomíes is located in the Central Altiplano. Otomi languages are spoken in Hidalgo (41 municipalities), western Mexico State (25 municipalities), northern Veracruz (12 municipalities), northern Puebla (11 municipalities), Queretaro (six municipalities), southeastern Guanajuato (five municipalities), east of Michoacán (one municipality) and east of Tlaxcala (one municipality). That is why we design a virtual platform that helps the preservation and strengthening of the indigenous Otomí language in an interactive and dynamic way. Through the implementation of games that will be based on the grammar, vocabulary and pronunciation of the indigenous language. A virtual platform is that "a flexible, individualized and interactive proposal, with the use and combination of diverse materials, formats and supports of easy and immediate updating defined by Susana Pardo ICT represent a useful strategy for the preservation of indigenous languages, mainly to those who are threatened to disappear.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".