The Primacy of L1-Based Cohesive Devices Over the Organization of Ideas in the Spoken English of Chinese EFL Learners
Bibliographic record
Abstract
L1 influence research on L2 learners’ spoken performance has focused on learners’ use of L1-based cohesive devices and propositional organization. The problem in these studies was that even though L2 learners were using L1-based cohesive devices, they were not making any grammatical or pronunciation errors, but their L2 speech patterns were not consistent with native speaker standards. This study investigated the ways in which 6 Chinese English as a foreign language (EFL) learners were influenced by their L1-based cohesive devices and organization of ideas during 30 hours of face-to-face interactions with 2 English native speakers. Data analysis involved transforming the transcribed data of interactions into a system of codes and categories (Corbin & Strauss, 2015), and the Excel software was used to generate the means, percentages, and ranks of different categories. Data analysis determined that Chinese L1-based cohesive devices and organization of ideas were manifested in the 6 Chinese participants’ speech as a coherent system of communication. Moreover, the researchers found that the most frequently-used L1-based cohesive device in the Chinese students’ L2 speech was the use of connectors which were employed to “add” new points to the speakers’ arguments. Implications for pedagogy included action research projects to scrutinize the introduction of a series of communicative tasks in the classroom that utilize scaffolding to highlight L1-L2 differences. The aim of these tasks is to raise students’ consciousness and help them “notice the gap” between L1-L2 discourse systems in the use of cohesive devices and organization of ideas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".