Comparing explanatory principles of complement selection statistically: a case study based on Canadian English
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it