Locatives and existentials in L2 Spanish: The acquisition of the semantic contrasts among <i>ser, estar</i> and <i>haber</i>
Bibliographic record
Abstract
This study analyses the expression of locative and existential predicates elicited through an oral production task in the speech of two groups of learners of Spanish as a second language (L2) (first language English, n = 18; first language Moroccan Arabic, n = 14), and a native control group ( n = 18). A total of 25,000 words were analysed, with over 1,000 locative and existential predicates. These predicates were coded according to the lexical verb used as well as the semantics of the theme; special attention was given to the use of copular verbs. Results indicated a delayed development of estar to express location, some overextension of haber with definite themes, and a small incorrect use of ser to locate objects in the English group. Overall, it is argued that given the case of complex semantics but simple syntax, the phenomenon is relatively unproblematic for L2 learners even at intermediate proficiency, probably due to the fact that these semantic concepts are already present in the first languages (L1s) of the learners, and particularly because these are mapped onto lexical pieces and not onto functional morphology, the bottleneck for L2 acquisition.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".