An Experimental Investigation of Mass Noun Types and Article Usage
Bibliographic record
Abstract
Speakers of languages with article systems have to make different article choices in the case of mass versus countable nouns. This study addressed article use with different types of mass nouns (liquid, solid and abstract). It investigated: a) whether first language (L1) Arabic speakers used English articles accurately with mass nouns; and b) whether they were sensitive to different types of mass noun. To address these issues, the study recruited twenty-seven English as a Foreign Language (EFL) Saudi-Arabic speaking participants and five native speakers of English, who formed a control group. Members of the experimental group were proficient to the elementary level, according to the Oxford Quick Placement Test. A written forced-choice elicitation task was administered to test their article use. The findings showed that: a) the Arabic speakers performed similarly to the native speakers of English in liquid contexts, but differently in solid and abstract contexts; b) the Arabic speakers did not perform similarly across all types of mass nouns, as they were sensitive towards mass noun types; c) their article use was more accurate in liquid contexts than in solid and abstract contexts; and d) they faced difficulties using articles with mass nouns that can be pluralised in Arabic. These findings indicate that the use of articles with mass nouns should be examined in the light of their subtypes, as well as whether second language (L2) learners’ L1 pluralise them or not.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".