Bibliographic record
Abstract
The syntax and semantics of verbs related to sensory perception has been a continuing subject of investigation in the field of linguistics. In terms of syntax, defining what types of grammatical arguments these verbs take and how and why the types of these arguments vary among perception verbs have been the main topics of discussion. In terms of semantics, the focus has primarily been on determining the thematic roles of the arguments of perception verbs and, relatedly, on determining what relationship they have to the event that they predicate of. This paper makes three main contributions. First, we present a novel analysis of perception verbs in Persian, a significant number of which feature complex predicates. In doing so, we encounter two main challenges: 1. The requirement for a general syntax/semantics for complex predicates that works in both perceptual and non-perceptual contexts; and 2. A generalized analysis that accounts for semantic entailments (which we here discuss only in the context of perception verbs). Second, in meeting challenge 1, we provide a novel account of Persian complex predicates using Glue Semantics. Third, we discuss how the makeup of Persian complex predicates provides significant insights into the overall conceptual/argument structure of perception constructions more generally, especially with regards to languages, like English, where this is hidden by fuller lexicalization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".