The distributional residue in Natural Phonology and its implications for morphologization
Bibliographic record
Abstract
Since at least Kruszewski (1881) it has been taken as an important task to sort out the alternations that involve morphology from those that are purely phonological. This dichotomy is largely followed by Natural Phonology (NP, cf. Donegan and Stampe 2009 for example), and by Generative Phonotactics (Singh 1987). Both these approaches insist on a strict delimitation (not a gradient one) between phonological and morphological phenomena. In this paper, I will first re-examine the problem of domain delimitation (Singh 1991) within NP by bringing in a more systematic use of the criterion of semioticity, which is not as often cited (but see Dressler 1980; Zwicky 1982; Ford and Singh 1983) but deserves attention. In order to do this, it will help to look at a case that is universally deemed to be clear: Final Devoicing in German. Because the delimitation of phonology from morphology is essential both for synchrony and diachrony (to classify alternations and to understand their transitions from one module to the other), I will then turn to diachrony for additional support for the criterion of semioticity as well as spell out how it can help us understand the phenomenon of morphologization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".