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Record W2146728449 · doi:10.5539/ass.v8n7p20

An Investigation into Verb Direction in English and Persian

2012· article· en· W2146728449 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsPersianTransitive relationModal verbVerbComputer scienceSentenceFocus (optics)BulgarianNatural language processingObject (grammar)Subject (documents)Collocation (remote sensing)Artificial intelligenceTransformation (genetics)MathematicsPhilosophyPhysics

Abstract

fetched live from OpenAlex

This paper compares several types of verbs in English and Persian in terms of direction. The direction of verbs seems to be potentially problematic for the Iranian EFL learners. English verbs can be formed by affixation and compounding. Even proper names and names of animals and products can be used as simple verbs. Persian is poor in this respect, and most verbs are formed via a limited number of affixes or by the productive process of verb collocation. English verbs seem more flexible in switching to intransitive or transitive modes, while Persian requires morphological transformation. The use of prepositions with objects can pose problems for the Iranian EFL learners. In one language, the focus of the sentence is on the subject while in the other, emphasis is directed to the object. Adverbs and prepositional phrases can be inherently stored in the English verb, while Persian has to openly express them.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.579
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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