Semantically-based functions of noun-class markers in Tagbana
Bibliographic record
Abstract
Abstract This paper addresses the use of noun-class markers in Tagbana from the perspective of a cognitively-inspired approach based on Langacker’s (2000. Grammar and conceptualization . Berlin & New York: Mouton de Gruyter) semiological principle. Drawing on this basic tenet of Cognitive Grammar according to which the symbolic function of language consists in making speakers’ conceptualizations auditorily or visually perceptible, it demonstrates that in syntactic constructions composed of ‘noun-stem+noun-class marker’ and ‘noun-class marker+identifier’, noun-class markers fulfil the semantic function of making explicit the way the speaker conceives of the experiential entity referred to in the utterance.This view goes beyond form-centred functions such as referent-tracking to include the signifying of complex conceptualizations involving more than one noun-class marker with the same noun-stem, as well as the discourse functions of indicating topicality, insistence on a referent’s existence and contrastive focus.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".