Layered feet and syllable-integrity violations: The case of Copperbelt Bemba bounded tone spread
Bibliographic record
Abstract
Abstract We identify evidence supporting two amendments to standard metrical theory: the inclusion of layered feet, and the allowance of syllable-integrity violations, where a foot parses some, but not all, of a syllable’s constituents. The evidence comes from a High tone spreading process attested in Copperbelt Bemba (CB), which as reported by Bickmore and Kula (2013) et seq., occurs over a ternary domain. In quintessentially metrical fashion, the domain is sensitive to the presence and position of heavy syllables. Thus, we argue that metrical theory should take the CB data into account. CB ternary spreading can occur in contexts with an abundance of unparsed syllables on either side of the domain. We argue that this property is problematic for ‘Weak Layering’ accounts using binary feet (McCarthy and Prince 1986; Hayes 1995), which revolve around the minimal presence of unparsed syllables. We propose an alternative account using layered feet (Martínez-Paricio and Kager 2015), specifying an inner quantity-sensitive iamb and a strictly monomoraic adjunct. We show that a principled characterization of the spreading domain is that tone associates to all and only footed moras. We argue that a metrical analysis provides a more principled account of the data than can be achieved by Bickmore and Kula’s purely autosegmental analysis. Finally, we show that foot-based accounts of CB ternary spreading predict syllable-integrity violations (SIVs), where parsing consumes only the first of two tautosyllabic moras. Contrary to the common view that SIVs are universally disallowed, we embrace this result and put it in a typological context. We adopt an Optimality Theory constraint set to model SIVs (Kager and Martínez-Paricio 2018b), and extend it, paving the way for a typological investigation of SIVs.
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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 | 0.013 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".