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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".