Bibliographic record
Abstract
This paper presents new data on the encoding and linguistic construction of topological spatial relations.The claim is that most of the supposedly topological relations are rather subjective, contextualized and perspectivized.In order to give evidence, this paper surveys the conceptualization of topological spatial relations and the lexicalization and distribution of the various meaning components that go into spatial description.Additionally, this paper looks at the effects of and interaction among language, cognition, and perception in a variety of languages.The languages at focus are Dene Sqmin (Chipewyan), a polysynthetic Athapaskan language spoken in Cold Lake, Alberta (Canada), an agglutinative language, Upper Necaxa Totonac (Mexico), as well as various Indo-European languages (English, Norwegian, German).To gain natural language data, this paper draws on two elicitation tools.One was developed at the Max-Planck-Institute in Nijmegen, the Topological Relation Markers (TRM).The TRM test consists of 71 simple black-and-white drawings of various objects, e.g., a cup on a table.Participants are asked to react to the prompt "Where is object X?".Based on the TRM test, data from the Spatial Categorization test (SPACE) developed by the author will be presented.It is supposed to reveal some more insights into linguistic spatial categorization, and more specifically the categorization of topological spatial relations.Moreover, data will be presented on the lexicalization and distribution of the various meaning components that go into spatial description.The SPACE test consists of 95 simple video animations of various objects.The results of both tests support a distributional and only partially compositional view of spatial semantics.Moreover, the various meaning components that go into the encoding of spatial description in many languages are hard to pinpoint to a single morpheme or word, e.g., an adposition.For speakers of some languages, especially Dene Chipewyan and Totonac, seemingly static and objective scenes require morphosyntactic devices which signal perspective, level of specificity, motion, causation, and other rather 'non-spatial' meaning components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".