Bibliographic record
Abstract
'Non-native' words that do not follow the phonological rules posited strictly on the basis of the native elementsof a language often pose tremendous analytic dilemma. They are generally excluded from analyses:rarely are they considered as modifying the phonological system of the borrowing language. Turkish has borrowedimmensely from non-harmonic languages, but it is still analyzed as respecting its former harmonicconstraints. I will show that even if the question is well acknowledged in the literature, problems arise atmany levels of analysis because of specific views that are generally shared on the architecture of the languagefaculty. I will first claim that the synchronic data suggest that we must suppose at least co-existing allomorphicforms in the lexicon (suppletion) because of the reorganization of the phonological system that occurredfollowing borrowing. I claim that the so-called 'harmonic' allomorphy is at least morphologically conditionedand that the allomorphs are stored. But I will also show that even an analysis in terms of suppletion is notenough to handle the data: a word-based morphology is necessary to properly take care of the facts; modelsbased on morphemes and lexemes can not do so.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".