The<i>His</i>/<i>Her</i>Challenge: Making Progress in a ‘Regular’ L2 Programme
Bibliographic record
Abstract
This paper reports on two studies that investigated the effectiveness of a contrastive analysis type of pedagogical intervention, which aimed to promote interlanguage development in the use and understanding of English possessive determiners (PDs) among adolescent second language (L2) learners. The first research question asked whether explicit instruction involving a rule of thumb, contrastive information and repeated contextualised practice of the rule would allow learners with French and Catalan/Spanish native languages in ‘regular’ limited-exposure programmes to make progress in using and understanding English PDs. The findings in both contexts confirm that this type of form-focused instruction, which had proven successful in more acquisition-rich contexts, could also help school learners in ‘drip feed’ instructional settings extract greater benefit from their limited contact with the L2. The second research question asked whether the developmental framework for the acquisition of English PDs, originally created to account for francophone learners' acquisition, was valid for other L2 populations. The findings demonstrate that it also explains patterns of development among Catalan/Spanish learners. The results of the two studies point to criteria that may be relevant in determining the type, timing and approach for focused pedagogical intervention in limited-exposure programmes.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".