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Record W2331229229 · doi:10.1515/jsall-2015-0001

Retroflex consonant harmony: An areal feature in South Asia

2015· article· en· W2331229229 on OpenAlexaff
Paul Arsenault

Bibliographic record

VenueJournal of South Asian Languages and Linguistics · 2015
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsTyndale University
Fundersnot available
KeywordsPhonotacticsConsonantObstruentLinguisticsHarmony (color)Place of articulationGeographyPhonologyArtVoice

Abstract

fetched live from OpenAlex

Abstract Retroflexion is a well-known areal feature of South Asia. Most South Asian languages, regardless of their genetic affiliation, contrast retroflex consonants with their non-retroflex dental counterparts. However, South Asian languages differ in the phonotactic restrictions that they place on retroflex consonants. This paper presents evidence that a large number of South Asian languages have developed a co-occurrence restriction on coronal obstruents that can be described as retroflex consonant harmony. In these languages, roots containing two non-adjacent coronal stops are primarily limited to those with two dentals (T…T) or two retroflexes (Ṭ…Ṭ), while those containing a combination of dental and retroflex stops are avoided (*T…Ṭ, *Ṭ…T). Historical-comparative evidence indicates that long-distance retroflex assimilation has contributed to the development of this phonotactic pattern (T…Ṭ → Ṭ…Ṭ). In addition, the paper demonstrates that the distribution of languages with and without retroflex consonant harmony is geographic in nature, not genetic. Retroflex consonant harmony is characteristic of most languages in the northern half of the South Asian subcontinent, regardless of whether they are Indo-Aryan, Dravidian or Munda (but not Tibeto-Burman). It is not characteristic of Indo-Aryan and Dravidian languages in the south. Thus, retroflex consonant harmony constitutes an areal feature within South Asia.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.045
GPT teacher head0.375
Teacher spread0.330 · 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 designObservational
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

Citations4
Published2015
Admission routes1
Has abstractyes

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