The influence of aspect on the countability of Polish deverbal nominalizations: Evidence from an acceptability rating study
Bibliographic record
Abstract
Abstract The paper presents the results of a study investigating a possible influence of the viewpoint (perfective vs. imperfective) and lexical (telic vs. atelic) aspect of Polish verbs on the countability of eventive nominalizations (substantiva verbalia) derived from these verbs. Polishsubstantiva verbaliapreserve many properties of the base verbs, including the eventive meaning and aspectual morphology. Native speakers of Polish rated the acceptability of nominalizations in count and mass contexts. An effect of both viewpoint and lexical aspect was found in mass contexts, where aspectually delimited (perfective, accomplishment) nominalizations were less acceptable than non-delimited (imperfective, state) nominalizations. In count contexts, only an effect of the lexical aspect was clearly present, with accomplishment nominalizations being more acceptable than state nominalizations. The nominalizations were overall rated as more natural in mass than count constructions, regardless of the aspect. The results indicate that aspect plays a role in establishing the countability of a word, but it does not fully determine it.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".