Propositional attitude verbs and complementizers in Medumba
Bibliographic record
Abstract
We present the preliminary results of an investigation on complementizers and their interaction with propositional attitude verbs in Medumba (Grassfields Bantu, Niger-Congo). This initial sketch of the Medumba C-system opens up questions about the syntactic distribution and semantic force of the various Cs. There are two clause-initial Cs, /mbʉ/ and /ndà/, of which /mbʉ/ has three syntactically conditioned allomorphs: [mbʉ̀ ]-L, [mbʉ́ ʉ̀ ]-HL and [mbʉ̀ ʉ́ ]-LH. The clause-final [lá] obligatorily co-occurs with two of the clause-initial Cs, namely [mbʉ́ ʉ̀ ]-HL and [ndà]. Additionally, the inventory of propositional attitude verbs (pavs) is quite small, with only four identified thus far: two are monomorphemic ([lεn] ‘know’, [t͡ʃúp] ‘say’) and two are are bi-morphemic ([kwὲ-də̀ ] ‘think-iter’, [bέt-tə́ ] ‘ask- iter’). We make the case for a syntactically conditioned floating H-tone. Additionally, we propose a basic structure of the Medumba CP and raise questions about the scope of polarity and the nature of the clause-final particle /lá/. Keywords: Medumba, complementizer, propositional attitude, embedding
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".