Bibliographic record
Abstract
This work explores the argument structure of causatives and applicatives in Tshiluba, a Bantu language spoken in the Democratic Republic of the Congo. The data were elicited with a speaker of Tshiluba through storyboard elicitation and grammaticality judgement tasks. Consistent with preceding analyses of benefactives in Bantu languages (Pylkkänen 2002; McGinnis 2008; de Kind and Bostoen 2012, inter al.), this work supports the analysis that benefactives in Tshiluba are high applicatives. Cases where both causative and applicative morphemes are present provide evidence that the position of the applicative morpheme determines whether the construction is benefactive or malefactive. Within the framework of Distributed Morphology (Halle and Marantz; 1993, 1994), the applicative morpheme -el occurs as the head of a high ApplP, occasionally above vCAUSE and crucially always above Voice. This work shows that this is the case for both passives and antipassives of benefactives. Malefactives appear to be lower, as -el crucially occurs below vCAUSE and Voice in all cases. An unexpected pattern arises – Tshiluba double-object applicatives have been previously analysed as asymmetric in the positions available to each argument, in that the recipient indirect objects must follow the verb and precede the direct object (as in English, Bo grew Jo fruit vs. #Bo grew fruit Jo) (Dom et al. 2015). In the present data, direct objects and indirect objects can alternate in position.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".