An Analysis of Stative Verbs Used with the Progressive Aspect in Corpus-informed Textbooks
Bibliographic record
Abstract
This study was designed to investigate whether contemporary corpus-informed grammar textbooks written for English language learners and teachers presented the progressive use of stative verbs and if yes, which stative verbs were presented to occur with the progressive aspect and for which functions they took this aspect. A corpus of six electronic copies of corpus-informed textbooks was compiled and analyzed via AntConc. 3.2.4 text analysis program to identify types and functions of stative verbs and calculate their occurrences. Overall, textbooks differed in their treatment of the progressive use of stative verbs and inclusion of the variety and numbers of types and functions. One remarkable finding was that the stative verbs taking the progressive aspect in all textbooks were found to be associated with emotions (i.e. love) whereas those not allowing progressive use were related to cognition (i.e. know). Another remarkable finding was that the textbooks which presented the highest numbers of stative verb types provided the most diverse functions whereas the textbooks which included the least numbers of stative verbs provided one or no function. Findings are hoped to raise awareness among textbook writers in making use of both the communicative messages motivated by the progressive use of stative verbs and the frequency and saliency information based on the corpus of present-day English to help learners grasp the changes in the language use.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".