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Record W3033638861 · doi:10.1017/s0025100319000367

Kalasha (Bumburet variety)

2020· article· en· W3033638861 on OpenAlexaff
Alexei Kochetov, Paul Arsenault, Jan Heegård Petersen, Sikandar Kalas, Taj Khan Kalash

Bibliographic record

VenueJournal of the International Phonetic Association · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsTyndale UniversityUniversity of Toronto
Fundersnot available
KeywordsUrduLingua francaArabicLexiconLinguisticsVariety (cybernetics)Aryan raceLanguages of AsiaHistoryLanguage contactGeographyAncient historyArtificial intelligenceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Kalasha (ISO 639-3: kls), also known as Kalashamon, is a Northwestern Indo-Aryan language spoken in Chitral District of Khyber Pakhtunkwa Province in northern Pakistan, primarily in the valleys of Bumburet, Rumbur, Urtsun, and Birir, as shown in Figure 1. The number of speakers is estimated between 3000 and 5000. TheEthnologueclassifies the language status as ‘vigorous’ (Eberhard, Simons & Fennig 2019) but some researchers consider it ‘threatened’ (Rahman 2006, Khan & Mela-Athanasopoulou 2011). Kalasha has been in close contact with Nuristani and other Northwestern Indo-Aryan languages. Among the latter, the influence of Khowar has been particularly strong because it functions as alingua francaof Chitral District (Liljegren & Khan 2017). The Kalasha lexicon includes many loanwords from Khowar, as well as from Persian, Arabic, and Urdu (Trail & Cooper 1999). Early efforts to put the language in writing employed Arabic script but a Latin-based script was adopted in 2000 (Cooper 2005, Kalash & Heegård 2016).

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)0.000
metaresearch head score (Gemma)0.000
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 score0.046
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0460.011

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 head0.020
GPT teacher head0.280
Teacher spread0.260 · 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

Citations91
Published2020
Admission routes1
Has abstractyes

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