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Record W2497822514 · doi:10.1075/cll.29.09smi

Sri Lanka Malay

2006· book-chapter· en· W2497822514 on OpenAlexaff
Ian Smith, Scott Paauw

Bibliographic record

VenueCreole language library · 2006
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsMalaySri lankaGeographyEnvironmental planningLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Two sociolinguistic characteristics of Sri Lanka Malay (SLM) are atypical for a creole: a non-European, non-colonial lexifier (Vehicular Malay), and a single substrate (Sri Lanka Muslim Tamil) with which it remained in contact. The Tamil-like characteristics of present-day SLMdiffer from those often claimed to typify creoles. In particular, the SLMtense-mood-aspect system expresses Tamil categories rather than the traditional creole categories of anterior, nonpunctual and irrealis. Bakker (2000) claims 19th century SLM more closely resembled a ‘typical’ creole, and the Tamilization of SLM represents a recent process of ‘conversion’. However, present-day SLMdescends from (unrecorded) 19th century colloquial SLM, not from the written diglossic high. The Tamilized structure of SLM results more plausibly from its atypical developmental context than from recent influence.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.053
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0530.021

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 head0.029
GPT teacher head0.364
Teacher spread0.335 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations34
Published2006
Admission routes1
Has abstractyes

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