What Features Best Characterize Adult Second Language Utterance Fluency and What Do They Reveal About Fluency Gains in Short-Term Immersion?
Bibliographic record
Abstract
This study reports on how one can examine a second language (L2) speech corpus in order to define which of many possible features of L2 utterance fluency (i.e., speech fluidity) should be the focus of an L2 fluency gains investigation. Participants were 100 adult English-speakers enrolled in a French immersion program. Data from 50 randomly selected participants were assigned to Sample A for Analysis 1 and the remainder to Sample B for Analysis 2. In Analysis 1, 23 candidate speech features, drawn from the literature at large, were examined in Sample A through a series of logical and statistical steps and systematically reduced to four features as constituting a core set of L2 utterance fluency features. In Analysis 2, these four features were examined in the Sample B corpus for gains after 5 weeks of immersion. Results indicated strong gains on all four. In Analysis 3, by way of replication, we reversed the process by using the Sample B data to first define the target fluency features and then the Sample A data to test for fluency gains. The main results replicated those of Analyses 1 and 2. The four features that emerged as core L2 utterance fluency features were mean syllable run length and mean phonation run length between silent pauses, and mean syllable duration and mean silent pause duration. Mean filled pause duration did not meet the criteria for belonging to the same fluency construct. Overall, the results showed that it is possible (a) to operationally define L2 fluency markers without reference to fluency gains, and (b) to then use these fluency markers to study L2 fluency gains without the gains data having shaped the operational definition of fluency in the first place, thereby avoiding the circularity of post hoc identification of relevant variables.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".