Exclamative clauses in English and their relevance for theories of clause types
Bibliographic record
Abstract
In the present study, I investigate the grammar and usage of English exclamative clauses of the typeWhat a wonderful journey this is!andHow wonderful this journey is!Building on existing research, I argue that the exclamative clause type can be motivated both syntactically and semantically/pragmatically. In the main part of my study, I offer a usage-based analysis of English exclamative clauses drawing on data from theBritish National Corpusand theInternational Corpus of English, British Component. I consider 703 tokens ofwhat-exclamatives and 645 tokens ofhow-exclamatives. My analysis reveals that English exclamatives typically occur in reduced form lacking an overt verbal predicate, i.e.What a wonderful journey!orHow wonderful!I provide an explanation for the predominance of reduced forms based on the semantico-pragmatic properties of exclamations. Moreover, I argue that the usage properties of exclamatives render it a marginal clause type, as it is highly infrequent and predominantly appears in non-clausal forms. Usage data point to a cline of clause types as the more appropriate approximation of reality instead of the familiar distinction between major and minor clause types.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".