Semantics and Processing of Weak and Strong Definites in Colloquial Persian: Evidence from an Offline Questionnaire
Bibliographic record
Abstract
The main goal of the present study was to investigate the semantics and processing of three (in)definite markers used in the colloquial Persian.It was proposed that colloquial Persian morphologically realizes two definite markers, the null marker 'Ø' and the suffix '-e'.It was further proposed that these correspond to so-called "weak" and "strong" definites, respectively; the presuppositions of strong definites need to be satisfied by an explicit antecedent but the presuppositions of weak definites do not (Schwarz, 2009(Schwarz, , 2013)).It was also proposed that 'ye…-i' is an indefinite marker.This proposal is supported by introspective judgments, as well as by quantitative data from an off-line questionnaire study (building on Burkhardt, 2006;Hirotani & Schumacher, 2011) that measured the naturalness of sentences that only varied in NPs marked with 'Ø', '-e,' 'ye…-i' in contexts which (i) an antecedent was available (Given contexts), (ii) an antecedent was unavailable but could be accommodated (Bridged contexts), and (iii) an antecedent was unavailable and accommodation was unlikely (New).As expected, there was an interaction between CONTEXT and MARKER and in particular the most natural text was the one in which '-e' was used in a Given context (this was the only condition that satisfied all relevant constraints).specifically in (in)definites.Also, his generous financial support during my master's has greatly helped me focus on my research.He also generously provided the financial sponsorship for my experimental study.I also would like to express my deepest gratitude to Dr. Masako Hirotani for giving me the opportunity of working in a high level experimental environment, for providing me with the Lab facilities, for all of her generous and invaluable trainings, advice, feedback, and tips in doing research.I also would like to thank my committee members Dr. Erik Anonby and Dr. Kumiko Murasugi for their support and help and their invaluable advice and feedback.I also would like to thank Dr. Ana Arregui for drawing my attention to the weak/strong realization of definites cross-linguistically, and for her generous and insightful guidance.Thanks also
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".