MétaCan
Menu
Back to cohort
Record W3003163885 · doi:10.2478/linpo-2018-0013

Restructuring of the Iranian tense/aspect/mood system

2018· article· en· W3003163885 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLingua Posnaniensis · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsGrammaticalizationLinguisticsRestructuringPersianPresent perfectHistoryPast tensePhilosophyPolitical scienceVerb

Abstract

fetched live from OpenAlex

Abstract The purpose of this paper is to outline the fundamental grammaticalization and degrammat(icalizat)ion processes observable in the restructuring of the tense/aspect/mood systems of the West Iranian languages during their historical development. Their core aspectual systems will be presented as consisting of three categories: Imperfective, Perfective and Perfect. Special attention will be paid to the rise of the analytic Perfect in Middle Persian and its further development in Early New Persian and other West Iranian languages. It will be shown that the degrammation of the copula played a significant role in the formation of compound temporal (Perfect, Pluperfect) and modal categories (Evidential, Conjectural) in New Persian, Kurdish, Balochi and Tajik. The Evidential mode of New Persian is based on the analytic Perfect rafte ast ‘he is gone’ and it is found in all the three aspectual categories (Imperfective, Perfective and Perfect) and both voices. It is usually claimed that it developed in the Iranian languages probably under Turkic influence. We intend to address the contentious issue of syntactic borrowing in terms of language contact in another paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.217
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it