Person and Licensing in Georgian: Puzzles for Cyclic Agree
Bibliographic record
Abstract
Cyclic Agree and the Person Licensing Condition predict ungrammaticality for a wide range of argument combinations in Georgian, incorrectly, though both have been used to explain core argument agreement in the language. This problem is defused by positing a high phi-probe on T, in addition to a previously proposed low articulated probe on v. The higher phi-probe is independently observable as added verbal morphology: a suffix-ablaut system. The high phi-probe is obligatory and this creates interactions between agreement loci as a result of general mechanisms (probe unification). A comparison between Georgian and Basque reveals systematic differences attributable to a contrast in the distribution of obligatory probes: the Georgian phi-probe on T is obligatory, while in Basque it is added to satisfy the PLC. This explains a difference between the languages with respect to alignment: intransitive S in Basque is consistently tracked by inflection characteristic of the v probe, while intransitive S in Georgian is consistently tracked by inflection characteristic of the T probe. Details of the interactions between agreement loci support a view of cyclicity where syntactic operations apply freely up to convergence, suggesting that cyclicity follows from Minimal Search or No-Tampering rather than Earliness.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".