Bibliographic record
Abstract
Morphemes are sometimes expressed by elements that are less than full segments, and, in a given language, the position of these elements in a word may vary. A recent analysis of these ‘mobile morphemes’ claims that their distribution is best explained in an optimality-theoretic framework that incorporates a set of featural alignment constraints (Akinlabi 1996). This paper argues that featural alignment plays no role in the realization of ‘mobile morphemes’. Instead, it recognizes a set of licensing constraints that explicitly identifies where featural exponents of such morphemes may appear in a word. Crucially, these licensing constraints, unlike featural alignment, are not morpheme-specific and therefore enjoy cross-linguistic support. Analyses of Chaha labialization, Terena nasalization, High tone realization in the Edoid associative construction and Southern Sami vowel lowering in terms of licensing are shown to be superior to the alignment-theoretic ones on both descriptive and explanatory grounds.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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 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".