Bibliographic record
Abstract
Languages have different ways of counting nouns, involvinga variable combination of pluralisation, combination withnumbers, and quantification. Established cross-linguisticliterature in this field like that of Chierchia (2019) suggestsa tripartite typological division between number-marking(e.g., English), classifier (e.g., Mandarin), and number-neutral (e.g., English) languages. In any case, the literatureargues for certain universals irrespective of type like thedivision of nouns into number-counting (e.g., pieces of meat)and kind-counting (e.g., pork and beef). In comparison withcross-linguistic typology and neighbouring languages likeLingala, Tshiluba does show affinities with the number-marking category with categories like fluid substancesneither able to change class nor combine directly withnumerals. However, there are other affinities with number-neutral languages like in the interpretation of quantifiers forthese fluid mass nouns, in this case a buunyi which can mean“many” bottles of water or “much” water. Ultimately, thetypological system is present but the motivation for it is morediscursive in that there can be countable and uncountableiterations of words like tshi-manu “wall” as opposed tocertain words being inherently (un)countable.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".