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Record W4392773020 · doi:10.1080/02643294.2024.2315825

Phonological impairments in Hindi aphasics: Error analyses and cross-linguistic comparisons

2024· article· en· W4392773020 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCognitive Neuropsychology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsCollege of New Caledonia
FundersUniversity of Birmingham
KeywordsSyllabic verseHindiAphasiaLinguisticsVowelPsychologySyllableRepetition (rhetorical device)PhonologyVoiceCognitive psychology

Abstract

fetched live from OpenAlex

We assessed phonological and apraxic impairments in Hindi persons with aphasia (PwA) and compared them to Italian PwA reported in previous studies. Overall, we found strong similarities. Phonological errors were present across production tasks (repetition, reading and naming), most errors were non-lexical and, among those, a majority involved individual phonemes. There were significant effects of length, but not frequency. Hindi PwA, like the Italian PwA, showed strong effects of syllabic structure, with most errors occurring on consonants and weak syllabic positions, preserving syllable structure and simplifying phonemes or syllabic templates. These similarities were modulated by some language-specific patterns. Vowel insertions were more common in Hindi, possibly due to the presence of a central vowel, and segmental simplifications concentrated on marked aspiration and retroflection features. We hope our study will encourage further research in Hindi and other Indian languages. This will improve clinical diagnosis and our understanding of cross-linguistic differences.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.164
GPT teacher head0.479
Teacher spread0.315 · 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