A Sociocultural Perspective on Foreign Language Education in State Education Systems
Bibliographic record
Abstract
This article advocates for a sociocultural perspective on foreign language education policy and practice in state education systems. The article’s geographical focus is on selected countries in Asia, though the general arguments may also be applicable to other countries. It examines factors underlying the divergence between policy intentions and educational outcomes in contexts where English is the first, compulsory foreign language in schools and is typically seen as important to economic development in a globalized world. The article also explores inequality of achievement in the contexts under discussion, where the teaching of English can often be characterized as an impediment to educational success for children from economically disadvantaged backgrounds. A basic premise of sociocultural theory applied to foreign language education is that one cannot separate learners and teachers from the social worlds they inhabit. Hence, the article argues for educational policy and the consequences for practice to be viewed from an ‘ecological perspective’, one in which what happens between learners and teachers in classrooms can only be understood meaningfully when viewed as part of a social world which includes the school, the local environment, the wider society and the myriad of elements which comprise its social culture and cultural practices.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".