Two ways of encoding location in Greek: Locative applicatives and prepositions
Bibliographic record
Abstract
Cross-linguistically, oblique theta roles such as location can be encoded by both adpositions and applicative morphemes. In this paper we argue that Standard Modern Greek (SMG), a language that encodes location primarily with prepositions, has a set of morphologically complex predicates that consist of an intransitive verbal root and a locative prefix, and behave like locative applicative constructions. We argue that this prefix is a low applicative head, licensing the addition of a locative DP argument to the intransitive verbal root. Specifically, this applicative head: (i) case-and theta-licenses the added argument, but being void of phi-features, it blocks its cliticization; (ii) is distinct from a homophonous free standing P semantically and syntactically; (iii) is undergoing grammaticalization, as evidenced by the emergence of a novel configuration, in which the locative predicates combine with locative PPs that retrieve semantic and syntactic information of the locative prefix. Our findings show that applicatives may come in various flavors, that a language may use both analytic and synthetic devices to encode location which are not derivationally related, and that lexical/inherent case does not necessarily reduce to a PP structure.
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".