The Ins and Outs of spatial language: Pragmatics shapes early-developing, cross-linguistically robust encoding patterns
Bibliographic record
Abstract
Research on the language of space has uncovered a complex set of conceptual and linguistic factors affecting the nature, use and acquisition of spatial vocabularies across languages. Here we highlight the important but understudied role of pragmatic factors in how spatial relations are encoded across ages and languages. We focus on Containment ( in/out ) and Support ( on/off ) terms that can denote both static locations (‘places’: be in/out of X ) and dynamic motions (‘paths’: go in/out of X ). We offer a new pragmatic analysis of place-denoting out/off as ‘negative’ locatives and, as a result, predict that such expressions should have a restricted informational contribution (and use) compared to in/on . This prediction is confirmed in four experiments. In elicited production tasks with English-speaking adults and three-year-olds, out and off (unlike in and on ) are used extremely sparsely to describe static locations (Experiment 1) but quite frequently to describe dynamic motions (Experiment 2). When contextual support is present, the use of place-denoting out/off increases (Experiment 3). Similar patterns in the use of locatives are found in French, Greek and Turkish speakers (Experiment 4). We conclude that pragmatic factors produce striking, early emerging and cross-linguistically stable properties of spatial vocabulary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".