Linguistic characteristics of second language acquisition and first language attrition : Turkish overt versus null pronouns
Bibliographic record
Abstract
This thesis investigates the binding of overt and null subject pronouns in second language (L2) acquisition and first language (L1) attrition of Turkish. The aim is to provide a comparative investigation of language transfer effects in the ultimate state of the L2 and L1 grammar. More specifically, it examines transfer effects from English L1 and English L2 into the grammars of Turkish L2 and Turkish L1, respectively. In this thesis, I propose that the Subset Condition (Berwick, 1985; Manzini & Wexler, 1987) can account for transfer phenomena observed in both L2 acquisition and L1 attrition. I argue that the subset relation that holds between the L1 and the L2 can be a predictor for the extent and duration of cross-linguistic transfer in L2 acquisition and L1 attrition. In other words, whether or not a particular property will resist L2 acquisition and undergo L1 attrition can be determined by looking at the subset relationship between the L1 and the L2 with respect to that property. The prediction is that in configurations where the 'influencing language' (L1 in L2 acquisition and L2 in L1 attrition) is the superset of the 'affected language' (L2 in L2 acquisition and L1 in L1 attrition), L1 transfer effect will persist in L2 acquisition and we will see more signs of L2 transfer into the L1 grammar, resulting in more attrition effects. Pronominal binding is chosen to investigate such cross-linguistic transfer effects. English and Turkish differ with respect to governing domains and types of pronominals present in two languages. Turkish, being a pro-drop language, allows null subject pronouns in main and embedded clauses. It also has a special type of anaphoric pronominal, kendisi, for which English has no corresponding form. Two experiments were conducted to test L2 acquisition and L1 attrition of binding properties of Turkish overt and null subject pronouns under the influence of English. Participants included native English-speakers living in Turkey (end-state L2 Turkish speakers) and native Turkish-speakers living in North America (end-state L2 English speakers). Overall, results obtained from the two studies reveal cross-linguistic transfer effects in the manner predicted. In particular, properties of English overt pronouns (e.g., him/her) are transferred onto the overt Turkish pronoun o in L2 acquisition and in attrition, whereas properties of the Turkish null pronoun and the anaphoric pronominal kendisi are unaffected by English.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".