Bibliographic record
Abstract
While null subjects are a well-researched phenomenon in pro-drop languages like Italian or Spanish, they have not received much attention in non-pro-drop languages such as English, where they are traditionally associated with particular (written) genres such as diaries or are discussed under a broader umbrella term such as situational ellipsis. However, examples such as the one in the title – while certainly not frequent – are commonly encountered in colloquial speech, with first-person singular tokens outnumbering any other person. This article investigates the linguistic and non-linguistic factors influencing the (non-) realisation of first-person singular subjects in a corpus of colloquial English. The variables found to contribute to the observed variation are drawn from a variety of linguistic domains and follow up on research conducted in such different fields as first language acquisition (FLA), cognitive linguistics, discourse analysis, sociolinguistics and language variation and change. Of particular interest is the finding regarding the link between null subjects and complexity of the verb phrase, which patterns in a clearly linear fashion: the more complex the verb phrase, the more likely is a null realisation. Not discussed in this form before, this finding, given its high significance and its robustness in light of alternative coding, may prove to be an important candidate for inclusion in future studies on (English) null subjects.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".