Bibliographic record
Abstract
Abstract This paper presents an analysis of reflexives in Shona. Taking seriously the observation that the reflexive morphemezvi-is homophonous with one of Shona's object markers, I argue that this homophony is not accidental. Rather, the morpheme that emerges in reflexive contexts is object marking triggered by a covert anaphor. The analysis rests on two planks: first, establishing thatzvi-is the default agreement form in the language generally; and second, establishing that a covert anaphor may trigger such an agreement. In so doing, a treatment of object marking as the exponence of discourse-givenness is advanced. The analysis is compatible with treatments of object marking in Shona as either an agreement affix or a clitic. Theoretical issues related to default agreement, covert anaphors, and distinctions between discourse-givenness and topicality are also discussed, along with an alternative account treatingzvi-as a valence-reducing derivational affix.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".