Bibliographic record
Abstract
Abstract In this paper, we investigate the relation between head movement and the synthesis-periphrasis distinction in the verbal domain. We use the term synthesis to refer to verbal expressions in which the lexical verb bears all the verbal inflection in a clause (e.g. rode in English). In contrast, a periphrastic verbal expression additionally contains an auxiliary verb (specifically, be or have ), and verbal inflection is distributed between the lexical verb and the auxiliary (e.g. had ridden ). We argue for two crosslinguistic generalizations: AfTonomy and *V-Aux. According to AfTonomy, affixal Ts vary as to whether they are in a head movement relation with a verb. *V-Aux states that in periphrasis, the lexical verb and the auxiliary cannot be related by head movement. Existing analyses of periphrasis can account for one or the other generalization, but not for both. We further argue that this tension between the two generalizations is resolved if we adopt the hypothesis that both head movement and periphrasis are tied to selection. More specifically, we propose that head movement is parasitic on a selectional relation (following Svenonius 1994, Julien 2002, Matushansky 2006, Pietraszko 2017, Preminger 2019) and that auxiliaries are merged as specifiers selected by functional heads such as T (Pietraszko 2017, 2023).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".