Bibliographic record
Abstract
Abstract This article investigates definiteness and its interactions with demonstratives and number in Laki (Northwestern Iranian). By the examination of demonstratives and building upon previous proposals, I argue for two types of definite DPs in Laki, namely anaphoric and deictic. I show that the patterns of definite and number marking are sensitive to the type of the DP. In particular, I argue that double definiteness, resulting from an Agree relation between D and N, and head movement of Num to D both are obtained only in anaphoric definite DPs for feature-checking requirements. Overall, this study highlights the contributions of anaphoricity to the DP internal structure. The present proposal can account for similar phenomena in other Iranian languages (i.e., Sorani and Kermanshahi Kurdish). The divergence of Laki definiteness from similar attested patterns (i.e., Scandinavian double definiteness) contributes to our cross-linguistic understanding of definiteness and its interactions with other nominal elements.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".