Segmental and Prosodic Evidence for Property-by-Property Transfer in L3 English in Northern Africa
Bibliographic record
Abstract
In this paper, I argue in favour of property-by-property transfer in the third language acquisition of English by L1 Arabic and L2 French speakers in Northern Africa (Algeria and Tunisia) based on a reanalysis of previous work. I provide a phonological analysis of their spontaneous production data in the domains of consonants, vowels, stress, and rhythm. The L3 phonology shows evidence of influence from both L1 Arabic and L2 French, with mixed influences found both within and across segmental and prosodic domains. The vowels are French-influenced, while the consonants are Arabic-influenced; the stress is a mixture of Arabic and French influence while the rhythm is French. I argue that these data are explained if we adopt a Contrastive Hierarchy Model of feature structure with the addition of parsing theories such as those proposed by Lightfoot. These data provide further evidence in support of the Westergaard’s Linguistic Proximity Model. I conclude by showing how this approach can allow us to formalize a measure of linguistic I-proximity and thus explain when the L1 or L2 structures will transfer.
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 | 0.001 | 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) | 0.004 | 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".