Eliciting Production of L2 Target Structures through Priming Activities
Bibliographic record
Abstract
Abstract: This study focuses on the pedagogical applications of structural priming research in an English for academic purposes (EAP) context, investigating whether priming activities are an effective tool for eliciting production of target grammatical structures. University students across four EAP classes carried out a total of 6 information-exchange activities over a 13-week semester. Some students received information-exchange activities that provided models of the target structures (e.g., primes), while other students received activities without any models. Analysis of the students’ novel utterances generated from sentence fragments (e.g., prompts) indicated that students who carried out the priming activities produced more target structures following prime sentences than they did in the absence of primes. In addition, students who did priming activities produced significantly more relative clauses and adverbial clauses overall than did students who carried out the activities without models, but there was no difference in their production of passives. Implications and suggestions for instructors interested in incorporating priming activities in L2 classrooms are discussed.
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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 | 0.006 |
| 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 | 0.002 |
| 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".