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 '' 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 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 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).
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".