Young Learners and Lexical Awareness: Children's Engagement With Wordlists and Concordances
Bibliographic record
Abstract
Sinclair (1991) found that lexical analysis can be overcomplicated, yet Johns (1994) called for investigation into whether corpus analysis can motivate beginners and near‐beginners. The findings of this research suggest that young EFL learners can enjoy using corpus analysis tools (wordlists and concordances) to identify, classify, and generalize about English. The teacher‐researcher developed a series of classroom activities in which students created wordlists and discovered and recorded collocations. Quantitative data gathered over 1 year from yes/no and ranking questions indicate that students found the activities enjoyable, and 230 write‐in comment sheets provided qualitative data that support the motivational impact of the activities. Findings suggest that as corpora, particularly of children's language, are created and expanded upon, it is vital that classroom activities developed to target meaningful young learner language present this relevant language in an enjoyable, engaging manner.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".