Effects of Formal Instruction and a Stay Abroad on the Acquisition of Native-Like Oral Fluency
Bibliographic record
Abstract
The study describes the effects of formal instruction (FI) and a stay abroad (SA) on the fluency displayed by 19 bilingual EFL undergraduate non-native speakers (NNSs). It includes data from 10 native speakers (NSs). The relative frequencies of seven dysfluency phenomena at three data-collection points are compared statistically, and a linear regression analysis is performed between NS and NNS data. A strategic change is revealed. After FI, learners adjust their speech to an NS pattern, but disruptions – especially self-repetitions, pauses, and non-lexical fillers – are still frequent. The SA serves to correct this somewhat while maintaining the NS-like tendency. There is a decrease in the number of phenomena that may be perceived as signs of insecurity, producing the impression of more fluent speech. These phenomena are replaced by increases in lexical fillers that may make NNS speech appear lexically richer. Both FI and SA, therefore, are shown to be positive contexts of acquisition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".