Reengineering English Language Teaching: Making the Shift towards ‘Real’ English
Bibliographic record
Abstract
This article underscores the importance of keeping up to date with vocabulary which is currently employed in English-speaking countries. It argues that textbooks, dictionaries and even corpora are not the most reliable sources to do this, and puts forward a pedagogical proposal – grounded in the Lexical Approach and three pedagogical innovation projects – to incorporate ‘real’ English into the language classroom. After clarifying what is meant by such ‘real’ English expressions and providing a possible classification for them, it suggests diverse sources of ‘real’ English input – including telecollaboration, sitcoms and TV series, podcasts, Internet texts, and recent bestsellers –, and subsequently presents a set of tried-and-true activities to exploit them, activities which allow the incorporation of pedagogically innovative approaches into the ELT classroom. The ultimate aim is to link the classroom with what goes on beyond its confines and to make our students’ lexical competence approximate that of native English speakers.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".