Kinds of the Attribute in the Mari Language
Bibliographic record
Abstract
This article is devoted to research of the kinds of the attribute in the Mari language. According to the different features five kinds of the attribute are distinguished. They are expanded and not expanded; isolated and not isolated; prepositive and post-positive; coordinated and uncoordinated; homogeneous and inhomogeneous. In the Mari language attributes can be subject to semantically equal and unequal dependent words in the same or different morphological forms. Separation of the Mari attributes occurs in the case of postposition and there is also its coordination with the determining word. In the Mari language determining of the postpositional attributes is associated with emotional coloring of speech. During the research it was found that the preposition and postposition of the attribute characterize them as uncoordinated and coordinated. The homogeneous attributes are different from inhomogeneous ones in that they are connected with coordinative bond and they exercise identical syntax functions and characterize person or object on the different sides. All these kinds of the attributes give the most complete picture of parts of the sentence in the Mari language.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".