Comments on Nordhoff ’s “Establishing and Dating Sinhala Influence in Sri Lanka Malay”1
Bibliographic record
Abstract
Students of Sri Lanka Malay agree that the language has been heavily influenced by the local languages, Sinhala and Tamil. Differences arise over not only the degree and timing of such influence from each language, but also the extent to which the language developed through untutored second language acquisition (on the part of Tamil &/or Sinhala speakers) &/or intense bilingualism (on the part of Malay speakers). Nordhoff’s arguments for Sinhala influence are examined in the context of Thomason’s (2001) framework for establishing contact-induced change and found to be convincing for some features, but weaker or unconvincing in others. The argument for early Sinhala phonological influence is based on an unsurprising distribution and the mechanism of substrate influence (Siegel, 1998, 2008) which has not been shown to operate in the context of intense bilingualism. The linguistic differing consequences of untutored second language acquisition and intense bilingualism have not been thoroughly investigated, except on lexicon (Thomason and Kaufman, 1988). The Sinhalese component of Sri Lanka Malay lexicon stands at less than 1% (Paauw, 2004), a figure inconsistent with the claim of heavy Sinhala influence through intense bilingualism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".