Demonstratives locate referents in common space and ground: A comparative syntactic approach
Bibliographic record
Abstract
Abstract Demonstratives can be used to locate a referent in space but they can also be used to refer to discourse referents located in the common ground. In English, proximal demonstratives are used for novel referents (indefinite specific this) while distal that can be used to refer to familiar referents. The empirical goal of this paper is to explore whether a similar pattern is found in other languages. To this end, we explored the use of demonstratives in 22 languages and found that this correlation is robust: if demonstratives are used for both spatial and grounding purposes, it is always the proximal demonstrative that is used for novel discourse referents and the distal demonstrative that is used for familiar discourse referents. The cross-linguistic solidity of this ‘Spatial-Grounding Correlation’, invites the conclusion that it is grammatically conditioned. We develop an analysis according to which the grounding use of spatial demonstratives is syntactically derived, utilising the Nominal Interactional Structure, independently motivated in Ritter and Wiltschko (2018, 2019, 2024).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".