The Acquisition of French (L3) Coda Consonant Clusters by English (L2) Learners Speakers of Persian (L1): An Optimality Account
Bibliographic record
Abstract
The present study presents an Optimality Theoretic account of syllable codas in French by the learners whose first and second languages are Persian and English respectively. Additionally, it investigates transfer at the L3 initial state, testing between the three hypotheses of Full Transfer/ Full Access (Schwartz & Sprouse, as cited in Özçelik, 2009) i.e., the main L1 transfer effect, L2 Status Factor (Bardel & Falk, 2007, 2011) i.e., the main L2 transfer effect, and Cumulative Enhancement Model (Flynn et al., 2004) i.e., all previously known languages’ positive or neutral transfer effect. As a matter of fact, OT is also used to see whether it supports what is obtained through transfer effects or not. To do so, two groups of Persian native speakers, but with differing English proficiencies (lower-intermediate and upper-intermediate) that were at the initial state of acquiring L3 French were asked to complete two tests, namely oral judgment test and production test. The analysis of the data was done through the mixed between-within subjects ANOVA. Results of the transfer effect provided a major role for the “L2 status factor”, while casting doubt on the tenability of several aspects of the CEM and provided no support for the FT/FA hypothesis. Regarding OT, the following constraint hierarchies were obtained for OJT and PT respectively: MAX-IO>> DEP-IO>>COMPLEX>> INDENT-IO and DEP-IO>> MAX-IO>> INDENT-IO>> COMPLEX. In fact, these rankings, especially the latter one, advocated the L2 constraint hierarchy and this was in accordance with the results of cross-linguistic effect, providing a major role for the L2 status factor.
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 | 0.002 |
| 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 | 0.000 |
| 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".