Research in foreign language education in Hungary (2006–2012)
Bibliographic record
Abstract
In the past quarter century, Hungary has offered fertile ground for innovative developments in foreign language (FL) education. The appropriate, albeit disparaging, label applied to Hungary in the mid-1970s – ‘a land of foreign language illiterates’ (Köllő 1978: 6) – no longer applies. In the wake of the dramatic changes of 1989, the number of FL speakers rose quite rapidly. As a beneficial side-effect, applied linguistic and language education research, areas which used to be relegated to the lowest rung of the academic ladder, began to be recognised as legitimate fields of scientific inquiry, offering young researchers the opportunity to embark on an academic career. As a result, Hungarian authors are now regular contributors to distinguished journals, and researchers from Hungary are welcome speakers at international conferences. However, Hungarian authors often choose to publish their research studies in local journals and volumes which are not easily accessible to the international research community, especially if written in Hungarian. The aim of this review, therefore, is to give an overview of such studies to demonstrate the breadth and depth of recent research conducted in Hungary.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".