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'.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.099 | 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 teacher head, 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".