Comparing explanatory principles of complement selection statistically: a case study based on Canadian English
Bibliographic record
Abstract
Several factors have been identified in the recent literature to explain variation in the selection of sentential complements in recent English, and the article begins with a survey of such factors. The article then offers a case study of the impact of such factors on non-finite complements of the adjective afraid on the basis of the Strathy Corpus of Canadian English. Attention is paid for instance to the Extraction and Choice Principles, passive lower predicates, and text type. Multivariate analysis is applied to compare and to shed light on such different explanatory principles. The Choice Principle proves to be by far the most significant predictor of the alternation, while the heavily correlated syntactic feature of Voice appears non-significant. Fiction, as opposed to the informative registers, shows a notable preference for to infinitives, though this finding needs to be replicated in datasets where controlling for author idiolect is possible. Theoretically plausible odds ratios are observed on the Extraction Principle and negation of the predicate, but they are not statistically significant. In the former case, this may well be due to the variable’s collinearity with the Choice Principle and its low overall frequency, resulting in a low effective sample size.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".