Bibliographic record
Abstract
This study presents an analysis of the semantics of the Persian object marker -râ.Semantically, -râ has been identified with definiteness marking (Sadeghi, 1970;Vazinpour, 1977), specificity marking (Browne, 1970;Karimi, 1989 Karimi, , 1990Karimi, , 1996 Karimi, , 2003aKarimi, , and 2005) ) and presupposition marking (Ghomeshi, 1996;Ganjavi, 2007).In this study, I challenge the assumptions presented in previous works and argue that while definiteness, specificity and presupposition capture important aspects of the meaning of -râ, none of them adequately characterize its semantics.Specifically, I argue that a unified account can be given if we assume that -râ is a maximality operator which picks out the maximal member of the denotation of its argument following Link (1983) and Beck and Rullmann (1999).The maximality proposal can account for the appearance of -râ on question words, contrastive topics, donkey sentences, plurals and indefinites which have remained unexplained in previous accounts of -râ.
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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 | 0.002 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".