Bibliographic record
Abstract
Cross-linguistically, personal pronouns are noted as being deficient in relation to some morphosyntactic and phonological properties. Some striking asymmetries have been identified between strong and weak personal pronouns in relation to modification, coordination/conjunction, whether they have a semantic referent, and can encode focus. This study explores the personal pronominal system of Dagbani along Cardinaletti and Starke’s (1994) typology and observed asymmetries. Using insights from published literature on Dagbani pronouns as well as my understanding as a native speaker, I argue that, unlike personal pronouns in Romance/Germanic languages, Dagbani personal pronouns can be modified by quantifiers, can be coordinated, and can occur in conjunction constructions, as well as encode topic and focus as salient semantic discourse properties. Furthermore, the pre/post verbal distinctions among nonemphatic pronominal forms in Dagbani still hold, even as these occur in coordinated and modified constructions, due to structural constraints imposed on them by coordinating conjuctions and quantifiers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".