Teaching spanish to migrants in the Soconusco region, Chiapas, Mexico
Bibliographic record
Abstract
This research was conducted in the state of Chiapas, throughout the Soconusco region. Students from BA in English Language Teaching at UNACH (Universidad Autonoma de Chiapas) participated as teachers in basic Spanish courses for the migrant population, from January 2020 to April 2021. This paper aims to give an account of the partial results of a study; its main objective is to analyze the nature of teaching Spanish to migrants and people in mobility from different countries. Migratory movements are a phenomenon of special incidence in contemporary societies according to the Manifest of Santander (2009), one of the first works carried out in Spain in terms of language teaching to migrants and refugees. Given the need to incorporate the teaching of the target language into the global policies of care for immigrants, since their learning of the target language depends a lot on the active insertion in the host community (Manifest of Santander cited in Garcia et al. 2014). The Language school at the UNACH is a public educational institution with social responsibility that has collaborated with COMAR (Comisión Mexicana de Ayuda a Refugiados) and the United Nations High Commissioner for Refugees (UNHCR) seeking to protect the rights and welfare of refugees, migrants and people in mobility during their stay in Mexico. Thus, the Language School has carried out the implementation of teaching basic Spanish courses as a foreign language to refugees and migrants from countries such as Haiti, Surinam, the United States, Jamaica, Switzerland, Saudi Arabia, and Canada. However, due to the COVID-19 contingency, these courses were changed from face-to-face sessions to virtual mode during that period and after the contingency, the courses had been taught in face-to-face sessions again. In this sense, this research yields interesting contributions that can be taken into account for a significant teaching-learning process of Spanish as a foreign language or as a second language in the region of Soconusco answering the following research questions: What is the training of teachers who teach Spanish as a second language in the Soconusco region? What difficulties did they have while teaching Spanish as a second language? How effective were Spanish classes for migrants and refugees?
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".