Russian and Negative Prefixing: A Cognitive-Semantic Approach to the Negative Adjective Prefixing in Russian, Spanish, Persian, and English
Bibliographic record
Abstract
Negative prefixing has always been an important and intriguing morphological process, through which adjectives are formed in many different languages. However, there are limits to negative prefixing. In this study, we introduce the novel concept of Polarity Flexibility, through which the limitations for the negative prefixing are accounted for. Furthermore, we conducted an experiment to investigate whether the PF is an active cognitive process. The results of the experiment confirm our hypothesis and the fact that Polarity Flexibility does indeed influence the cognitive processing. In our study, we introduce the notion of the syntactic arrangement which influences the negative prefixing. Therefore, we compare Russian, Persian, Spanish and English in negative prefixing to show how much the cognitive processes are influenced by the syntactic formations. Russian as a representative of Slavic languages brings an important insight into the way syntax plays role in the semantic-cognitive context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| 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".