Nominal linkers in Iranian languages: An introduction
Bibliographic record
Abstract
In some languages, a noun is linked to some of its modifiers and complements via a linking element. The cross-linguistic variation that is observed with respect to the properties and functions of nominal linkers has made their study an excellent area of research contributing to both the theory and the typology of linkers, and more broadly, to the study of nominals. Despite the importance of nominal linkers, however, a collection that solely focuses on this phenomenon has been noticeably absent in the literature. The use of nominal linkers is a prevalent feature in many Iranian languages. In this volume, we provide a description of a wide range of patterns of nominal linkers in eleven Iranian languages, delving into the intriguing behaviour of such elements and their interaction with other morphosyntactic markers. The papers in this volume are the result of research conducted under the “The Syntax of Nominal Linkers” project at the University of Toronto, funded by a Social Sciences and Humanities Research Council of Canada (SSHRC) Insight Grant (#435-2018-0527). To our knowledge, this volume is the first of its kind dedicated entirely to a single phenomenon in Iranian linguistics. We hope that this volume serves as a valuable resource for a wide range of readers, including researchers interested in nominal modification, nominal linkers, and nominal structures in general, as well as researchers and language instructors interested in Iranian languages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".