Bibliographic record
Abstract
Traditional grammar holds that parts of speech have broad semantic definitions: verbs are actions, nouns are entities, adjectives are states of being, and prepositions denote locations (Baker, 2003). This view is problematic, however, given that semantic concepts are lexicalized differently across languages. For example, through my field work with a native speaker of Kinyarwanda (Bantu family, spoken in Central Africa), I have found that in this language, states can be lexicalized as adjectives, nouns, or verbs: (1) umugabo ni munini “The man is big” ADJECTIVE MAN IS LARGE (2) imbwa n’ umweru “The dog is white” NOUN DOG IS WHITE (3) imbwa yera “The dog is white” VERB DOG BE.WHITE The state of “being big” appears as an adjective, while the state of “being white” can be both a noun (2) and a verb (3). The difference in category is appears to be motivated by the relative permanence of the state in question. A changing (or changeable) state is encoded as a verb (3). To reflect a permanent/unchanging state, an adjective or noun is used (12). Because the inventory of adjectives in Kinyarwanda is extremely limited, many “adjectival” permanent states are encoded as nouns. This alternation demonstrates the central role of Aspect (defined as a linguistic function that “characterizes the relationship of a predicate to the time interval over which it occurs” (Chung and Timberlake 1985:213)) in the lexicalization process. Aspect in Kinyarwanda takes the form of a verbal suffix, thereby necessitating the use of a verb when the state undergoes some sort of change. This phenomenon provides evidence that parts of speech are defined at least in part by a language’s syntactic requirements above and beyond broader semantic generalizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".