Representation and acquisition of stress: the case of Turkish
Bibliographic record
Abstract
This thesis investigates the representation and acquisition of word-level stress in Turkish. Two general proposals are made in the thesis, one related to formal phonology, the other about second language (L2) acquisition of word-level prosody. The first proposes that the presence/absence of the Foot is parametric; that is, contra much previous research (see e.g. Selkirk 1995, Vogel 2009), it is argued in this thesis that the Foot is not a universal constituent of the Prosodic Hierarchy; rather, some languages, such as Turkish and French, are footless. Several types of evidence are presented in support of this proposal, from both Turkish and French, with a focus on the former language. A comparison of regular (word-final) and exceptional stress in this language reveals, for example, that regular "stress" is intonational prominence falling on the last syllable of prosodic words in the absence of foot structure. Exceptional stress, on the other hand, is argued to be the result of certain morphemes coming into the computation already footed in the lexicon, and being footed on the surface, too, because of faithfulness to this information. The grammar, then, assigns the other properties of this foot, such as binarity and foot type, which are vacuously satisfied for regular morphemes, as they are not footed, and as the grammar has no mechanism that assigns feet or stress. The result is a unified analysis of regular and exceptional stress in Turkish.Second, the thesis proposes a path for the L2 acquisition of prosody, the Prosodic Acquisition Path Hypothesis (PAPH). The PAPH predicts different levels of difficulty and paths to be followed by L2 learners based on the typological properties of their first language (L1) and the L2 they are learning, and also on the basis of a hierarchical tree representation of the relationships proposed to hold between prosodic parameters. Most foot-related parameters are incorporated in the proposal, as well as the new parameter proposed in this thesis about the presence/absence of the Foot. The PAPH predicts that once the Foot is projected in an L1, learners of a footless L2 will not be able to expunge it from their grammar, but will, instead, be restricted to changing the values of foot-related parameters. Not every one of these parameters is, however, hypothesized to be equally easy to reset; depending on a variety of factors such as their location on the parameter tree and markedness, certain parameters, such as Foot-Type, are hypothesized to be easier to reset than others, such as Iterativity.The predictions as concerns the learning path are tested through an experiment, which examines productions of English- and French-speaking learners of L2 Turkish. The results of the experiment largely confirm the predictions of the PAPH. None of the English-speaking learners of Turkish were able to rid their grammar of the Foot, though they were able to make various Universal Grammar (UG)-constrained changes to their grammar, such as resetting Extrametricality from 'Yes' to 'No', and at later stages, Foot-Type from 'Trochaic' to 'Iambic', thereby having increasingly more word types with final stress. French-speaking learners, on the other hand, produced target-like footless outputs, with word-final prominence, from the initial stages of acquisition. At no stage did any of the learners have UG-unconstrained representations such as weight-insensitive iambs, which are not permitted by the inventory of feet provided by UG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".