El Idioma Inglés y los Factores que Influyen en su Proceso De Enseñanza – Aprendizaje en México
Bibliographic record
Abstract
Today the English language is considered the most important, although it is not the one with the largest number of speakers, it is the one that because of its use covers more places around the world. In Mexico, this language is also considered extremely important, where one of the main reasons is due to the country’s proximity to the United States, and which in turn derives in the Treaty between Mexico, the United States and Canada (T-MEC). Several programs were implemented to promote English language learning in public schools. For this reason, this article aims to show the different variables we find around the teaching process - learning English in Mexican schools. The method used for this research was the collection of research articles, review documents, web pages, newspapers etc. (Scielo, Dialnet, Google Scholar, Elsevier) that addressed the subject presented, so that, later, a reflection on the information gathered. The review showed that there are several factors that influence the teaching process - learning English has not had the expected results, among which include: the number of students in the classrooms; the teachers, from whom derive various problems such as the school preparation of the teachers, the overload of hours, the didactic preparation of their classes, the methodology used, as well as the lack of resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".