Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it