ESL Textbooks Materials and Real Language Use: Comparing Corpus-Based Materials and Textbook Materials on Gerunds/ Infinitives
Bibliographic record
Abstract
Acquisition of [verb] + gerund/infinitive-complement constructions can be problematic for ESL learners.Following a preliminary study identifying conflicts between rules presented on the complementation of several verbs in ESL textbooks and grammar references, this study compares gerund/infinitive verb complementation in English corpus data with their presentation in ESL/EFL textbooks and grammar references.We sought to resolve these conflicts using a corpus-based approach.Corpus-based research has looked at verbcomplementation of high-frequency verbs, yet these studies did not include the verb 'mean'.Findings reveal that complementation type following 'mean' depends on which of the 11 senses of the word is being used.Additionally, frequency of usage of these senses varies significantly between the two corpora.We highlight the utility of corpora to inform practitioners about the use of gerunds/infinitive complements in general and for 'mean'.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".