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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".