The rise of the analytic Perfect aspect in the West Iranian languages
Bibliographic record
Abstract
Abstract This paper focuses on the long-term grammaticalization of tense/aspect systems in the West Iranian languages, beginning with Old Iranian ( Section 1 ). In Middle Persian ( Section 2 ) the Aorist and the reduplicative Perfect of Old Persian were replaced by a new system of analytic constructions. The fundamental mechanism in the rise of the innovative Preterit (perfective) and Perfect categories was the process of grammaticalization, reducing the auxiliary ‘be’ into suffixes of the innovative Preterit. In Early New Persian ( Section 3 ) an unambiguous Perfect was recreated by attaching personal suffixes to the Perfect stem. In the second part of the paper ( Section 4 ) we turn to the elaboration of the evidential (‘non-witnessed’) subsystem in New Persian through grammaticalization and possible Turkic influence. A typological parallel in the southernmost Slavic languages ( Section 5 ) is provided.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".