Bibliographic record
Abstract
Null subjects in English(es) are a phenomenon that has recently received much attention in the specialized literature. However, most studies are based on small datasets and samples of varieties due to the difficulty of extracting null subjects from corpora. The present paper is a first step towards the automatization of the data retrieval process of null subjects and analyzes a much larger sample of cases and varieties than previous research, namely, Australian, Canadian, Jamaican, Singaporean, Nigerian, Indian, Bangladeshi and Pakistani Englishes. By focusing on referential and non-referential third person singular clause initial null and overt subjects, a variationist examination of the data is conducted by means of mixed-effects logistic regression analyses which shows that non-referential null subjects are a much more pervasive and stable phenomenon in World Englishes than their referential counterparts. In addition, a cline of varieties emerges with respect to referential null subjects: these null subjects are more frequent the more advanced varieties are in Schneider’s Dynamic Model.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".