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Record W3009066375 · doi:10.5539/ijel.v10n2p383

Spatial, Temporal and Structural Usages of Pashto Case Marker ‘Ta’

2020· article· en· W3009066375 on OpenAlexvenueno aff
Rajwali Khan, Muhammad Iqbal, Muhammad Anwar

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Linguistics, Cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsComputer sciencePhenomenonSyntaxNatural language processingArtificial intelligenceGeographyPhilosophy

Abstract

fetched live from OpenAlex

Pashto is an Indo-Iranian language which expresses different semantic aspects and functions with the help of case markers. The post-position & case marker Ta has the quality to posit many types of usages i.e. temporal, spatial and structural in Pashto language. In the research on Pashto syntax, Khan (2009), Roberts (2000) and Tegey and Robson (1996) have described the phenomenon of case marker ‘Ta’ but have not been investigated extensively. This study follows Ahmed (2006) to analyze in detail different functions and usages of Ta in terms of semantic and syntactic principles and provides a unified account for these diverse usages and their meanings. The analyses confirm that Ta as a case marker is not only used for Dative case as a subject but it has a polysemous nature in terms of spatial and non-spatial (structural, temporal) usages. The non-spatial usages are the extension of its spatial origin in the semantic field that is metaphorically spatial by default. Besides temporal and structural ­Ta usages can be extended to purpose, reason, obligations, immediate future, perception, ability and inability in relations to DAT (as a SUBJ or OBJ) in Pashto language.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.028
GPT teacher head0.265
Teacher spread0.238 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicLanguage, Linguistics, Cultural AnalysisFrench-language works237,207