City of Buenos Aires’s ELT landscape and its resilience against native-speakerism
Bibliographic record
Abstract
Scholarship on the Argentine English Language Teaching (ELT) context is scant; even more so is research addressing Native-speakerism in the Argentine context. This article fills an important gap pertaining to the understanding of the City of Buenos Aires-the largest city in Argentina- and its language education system. The data obtained via document analysis provides a description of the city’s population language level and language education infrastructure which in turn offers relevant insights regarding the city’s system’s strengths and weaknesses against Native-speakerism. The factors that render the City of Buenos Aires strong against Native-speakerism are (1) a public school system which requires qualified teachers; (2) an overall public/private school system which prefers qualified teacher with classroom management experience over proficient speakers of English with no teaching experience; (3) a legal hiring framework that favors the local labor force; (4) and availability of highly trained and respected English as a Foreign Language (EFL) teachers. All these strengths notwithstanding, weaknesses in relation to Native-speakerism exist within the medium/small private language school industry, which can (and might) circumvent the law to hire (under-qualified) NESTs illegally. The breeding ground for Native-speakerism in medium/small private language school calls for further research to delve into what extent Native-speakerism takes place in this specific context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".