Common names and proper nouns: Morphosyntactic evidence of a complete nominal paradigm
Bibliographic record
Abstract
The terms "common noun" and "proper name" encode two dichotomies that are often conflated. This paper explores the possibility of the other combinations—"common name" and "proper noun"—and concludes that both exist on the basis of their morphosyntactic behavior. In support of common names, inflectional regularization is determined to result from a "name" layer in the structure, meaning that common nouns that regularize are, in fact, common names (computer mouses, tailor’s gooses). In support of proper nouns, there are bare singular count nouns in English that receive definite interpretations and seem to be licensed as arguments by the same null determiner as proper names (I left town, she works at home). Not only does a four-way distinction between nouns, names, proper nouns, and proper names achieve greater empirical coverage, but it also captures the independent morphosyntactic effects of [PROPER] and [NAME] as features on D and N, respectively.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".