Syntactic categories informing variationist analysis: The case of English copy-raising
Bibliographic record
Abstract
This paper re-examines variation between the comparative complementizers (AS IF, AS THOUGH, LIKE, THAT, and Ø) that follow verbs denoting ostensibility (SEEM, APPEAR, LOOK, SOUND, and FEEL) in the large city of Toronto, Canada. Given that younger speakers appear to be using more of these structures in the first place, I evaluate the hypothesis that there is a trade-off in apparent time between these finite structures and the non-finite construction of Subject-to-Subject raising. Focusing on the verb SEEM, I find that the non-finite structures are losing ground in apparent time to the finite ones. I subsequently address the issue of how best to divide up the finite tokens as co-variants opposite the finite constructions, and find that a split according to syntactic properties – whether or not the copy-raising transformation is permitted – tidily accounts for the patterning and reveals a straightforward change in progress. The results reaffirm the value of using variationist methodology to test competing claims, and also establish that variation can behave in a classic way even among whole syntactic categories.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".