Are You the Type of Person Who Likes Other People? Extended Uses of the Reciprocal Particle ‐an in Kinyarwanda
Bibliographic record
Abstract
In linguistics, making a verb reciprocal means that one person does something to another person and that the other person does the same thing to the first person. Thus "John and Mary like each other" implies that "John likes Mary" and "Mary likes John." In most languages, one can only make a verb reciprocal when its subject is plural, so that "John and Mary like each other" is fine, but *"John likes each other" does not make sense. In Kinyarwanda, a Bantu language spoken in Rwanda, the reciprocal is indicated by the suffix ‐an on the verb, for example, Habimana na Mariya barakundana "Habimana and Maria like each other." However, the equivalent sentence with a singular subject, Habimana arakundana, does not have the nonsense interpretation *"Habimana likes each other" but rather the more intelligible "Habimana is the type of person who likes other people." Based on this rather unusual finding from field work with a native speaker of Kinyarwanda, this presentation explores the suffix ‐an in terms of the types of sentences in which it can and cannot appear in an attempt to generalize how it is used, the range of its meaning, and its possible relation with the preposition na "with."
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".