Generic Reference in English, Arabic and Malay: A Cross Linguistic Typology and Comparison
Bibliographic record
Abstract
According to the Longman Grammar of Spoken and Written English 1999 by Biber et al. (p. 266) generic article uses are more than twice as common in academic English than in conversation or fiction. This is an area that English for Academic Purpose (EPA) textbooks and teachers would need to target more than general English teaching. This paper is therefore a contribution towards better understanding of what linguistic facts about generics teachers and textbooks of EAP might need to cover in order to deal with them satisfactorily, particularly for learners with Arabic or Malay as L1. This paper is also significant as it is the first to compare the expression of generic meanings by noun phrases in three typologically quite different languages: the Germanic language English, the Semitic language Arabic and the Austronesian language Malay. The contrast between the three languages is substantial in that they have different settings according to the nominal mapping parameter (NMP), which captures some widespread generalizations about the occurrence of mass and countable nouns and articles in the languages of the world. As a part of a bigger project that investigates the acquisition and interpretation of generic reference by speakers of these languages, this article is descriptive and comparative in nature. The main finding is that the rules for mapping forms to generic meanings are more complex in English than in Malay or Arabic, in that English marks the difference between NP level and S level genericity and between established and non-established categories.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".