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Record W4283812562 · doi:10.1075/alal.22002.pos

Coastal toponyms of Iran

2022· article· en· W4283812562 on OpenAlexaff
Hamideh Poshtvan, Mahnaz Talebi-Dastenaei

Bibliographic record

VenueAsian Languages and Linguistics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsCarleton University
Fundersnot available
KeywordsTypologyToponymyPersianGeographyArchipelagoArchaeologyLinguistics

Abstract

fetched live from OpenAlex

Abstract While the domain of toponymy has great research potential, it has so far gained little attention in Iran. The present paper is a typological study on a sample group of toponyms from two different provinces of Gilan (a northern coastal province next to the Caspian Sea) and Bushehr (a southern coastal province beside the Persian Gulf) within the framework of the Australian National Placenames Survey typology proposed by Tent and Blair. The two regions in question are similar in terms of adjacency to a major water body but different in language, culture, and geography. To determine whether the Australian proposed typology is applicable to Iranian toponyms, we collected 60 coastal placenames from Gilan and Bushehr and classified them according to their Specifics and Generics based on Tent and Blair’s ( 2009 , 2011 ) typology. Further, we compared placenaming motivations and processes in the two regions. The results show that although the Iranian placenames differ from the Australian ones in terms of their structure, they fit well into the typology. Water-related features are among the top motivations for local namers in both Gilan and Bushehr. Nevertheless, Bushehr namers are more motivated by natural features while those from Gilan, in most cases, are non-naturally motivated.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.598

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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