L. V. Shcherba: a ‘new slant’ on modern foreign languages in the school curriculum?
Bibliographic record
Abstract
ABSTRACT In this paper, I offer a critical reflection on the thesis of the general educational value of foreign languages developed by Russian linguist Lev Vladimirovich Shcherba. I do so against the background of current debates on the positioning of foreign languages in the school curriculum in the United Kingdom (UK). I argue that Shcherba's thesis, which was developed almost a century ago, retains its currency and can make an important contribution to the on‐going discussion on the value of foreign languages in UK schools. The paper outlines Shcherba's scholarly explorations in general linguistics which underpin his arguments in favour of the inclusion of foreign languages in the basic school curriculum. The conception of language as a system immanently positioned in social experience assigns the foundational role to language in the literacy project. The conscious analytic processing of language phenomena is viewed as an essential pre‐condition of literacy, and foreign languages are shown to be instrumental in developing such an analytic capacity of mind. Shcherba's argumentation reflects a comprehensive and interdisciplinary approach, both to foreign language education and to curriculum matters, and merits the attention of language practitioners, educationalists and policy‐makers alike.
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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 | 0.003 | 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 | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".