The Acquisition of Definiteness and Specificity in English: A Case Study with Saudi-Arabic Learners of English Using an Online Task
Bibliographic record
Abstract
Definiteness with Arabic learners has been explored by many researchers such as Jaensch and Sarko (2009) and Sarko (2009). The majority of previous studies have used an offline task and focused on identifying the types of errors which learners were committing. Conversely, the present study will use an online reaction time task to investigate the learners’ accuracy in judging [±definite and ±specific] in a series of sentences. The aim of the study is to ascertain the accuracy of participants in judging grammatical and ungrammatical sentences in terms of definiteness and specificity in English, and also to identify which factors have the greatest effect on this accuracy. The study will examine the process of article acquisition from the perspective of universal grammar using the following hypotheses: The Representational Deficit hypothesis (RDH) by Hawkins and Chan (1997), the Feature Reassembly hypothesis by Lardiere (2009) and the bottleneck hypothesis by Slabakova (2008, 2009, 2015). Thirty-two Saudi learners have completed a grammatical judgment task that was designed using OpenSesame to incorporate a reaction time test along with two vocabulary tests (Yes/No and Lex30) and a proficiency test. The results showed no effect on definiteness and specificity with the Saudi-Arabic learners. Moreover, the findings demonstrated that there is no difference in reaction time which could be attributed to [±definite and ±specific]. Receptive vocabulary knowledge and proficiency affected the learners’ accuracy in judging article use in English, but no such effect was found for the learners’ productive vocabulary knowledge. Additionally, L1 negative transfer has been observed in Saudi-Arabic learners of English particularly with low-level learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".