Vestigial ergativity in Shughni: At the intersection of alignment, clitic doubling, and feature-driven movement
Bibliographic record
Abstract
This paper provides an account of two related aspects of the past-tense morphosyntax of Shughni (Eastern Iranian): (i) the use of second-position clitics, rather than the verbal suffixes of the present tense, to index past-tense subjects’ φ-features; and (ii) a curious alignment pattern – sometimes referred to as vestigial ergativity – in which third-singular subjects of transitive and unergative verbs, but not unaccusative verbs, trigger a second-position clitic matched to their φ-features. After applying a battery of diagnostics to the Shughni clitics, I argue that these morphemes are the result of a clitic-doubling operation rather than agreement proper. A significant clue for this conclusion is the lack of any morphological material co-indexing third-singular unaccusative subjects, which I take to indicate that the past-tense clitics, unlike the present-tense suffixes, lack a default morpheme. This account not only provides support for the validity of diagnostics developed by previous authors for object clitics, but also highlights the importance of including subject clitics when developing a theory of clitic doubling and agreement. In the latter part of the paper, I build upon recent work on the alignment system of Davani (Western Iranian) to provide a feature-driven movement account of Shughni syntax, whereby all unaccusative subjects except third-singular move to a phase edge, where they are found by a probe on T0 and trigger a second-position clitic bearing their φ-features.
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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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".