Perceptions of L2 Fluency by Native and Non-native Speakers of English
Bibliographic record
Abstract
This article explores perceptions of the speaking fluency of 24 adult ESL learners (11 men, 13 women) who narrated picture stories at Time 1 and again 10 weeks later at Time 2. One-minute excerpts from each rendition were randomized and played to 15 novice and six expert native speakers of English (undergraduate education students and experienced ESL teachers holding graduate degrees, respectively). Because of the increasingly frequent use of English among non-native speakers (NNSs) throughout the world, 15 advanced NNSs of English were also included in the study. All three groups of listeners rated and recorded their impressions of the fluency of the stimuli. The ratings of all three groups were highly inter-correlated at Times 1 and 2. Fluency ratings correlated with the temporal measures of total pause per second and pruned syllables per second. Pausing, self-repetition, speech rate, and fillers accounted for three-quarters of the negative temporal impressions recorded by listeners; salient non-temporal impressions included pronunciation, grammar, and vocabulary.
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 | 0.001 | 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 | 0.000 |
| 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".