What learning Latin verbal morphology tells us about morphological theory
Bibliographic record
Abstract
Abstract The Classical Latin verb has featured prominently in theoretical morphology. In particular, the notoriously unpredictable forms of the past participles that nevertheless show reliable syncretism with a semantically diverse set of deverbals challenge our notions about the relationship between form and meaning. The various treatments of this system disagree not only in their theoretical building blocks but also in their basic assumptions about what ought to be explained, which makes it difficult to properly evaluate them against one another. This paper aims to empirically motivate the prior assumptions about productivity and arbitrariness that drive these accounts. In applying insights developed for child language acquisition to a large Latin corpus, the theoretical frameworks are compared on equal footing. It becomes clear that the productive past participle forms do not line up well with the frequency-based assumptions of prior accounts and instead mirror the diachronic developments that the system underwent on its path to Romance. A new treatment is proposed to incorporate the acquisition results and to conform with diachronic outcomes. The methods developed here reveal explanatory gaps in the theories that had not previously been appreciated and emphasize the importance of quantitative evidence from a range of sources in future morphological analysis.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".