Singular and plural preferences among adjectival collocates of CAT and DOG
Bibliographic record
Abstract
The singular vs. plural distinction in English count nouns is not usually considered as being of any real consequence to the choice of adjectives accompanying these nouns. This study questions this assumption and explores the pre-nominal adjectives occurring with cat/cats and dog/dogs with a view to identifying the main patterns of co-occurrence with singular forms vs. plural forms. Attributive adjectives occurring before cat(s) and dog(s) were investigated, relying on a corpus of contemporary American fiction. Applying Distinctive Collexeme Analysis to the corpus results, it was found that coherent groups of adjectives occurred preferentially with the singular or plural of both words. Colour adjectives and evaluative adjectives like good, for example, occurred preferentially with the singular forms, while adjectives such as stray, wild, and feral occurred preferentially with plural forms. The usage differences observed in the data can be motivated by reference to a folk model of the world in which animals take their place as house pets, wild animals, or somewhere in between.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".