Experiential motivation and the linguistics of sitting, standing, and lying
Bibliographic record
Abstract
The three human at-rest postures of sitting, standing, and lying are basic, recurring features of human behavior and may reasonably be called primary postures. The three postures share the property of being stable through time, but they are also differentiated in terms of their overall shape, their physiological properties, and typical associated behaviors such as the association of sitting with social interaction, and lying with sleeping. The experiential realities of the three postures underlie and motivate a range of cross-linguistic phenomena involving morphemes with meanings of "sit", "stand," and "lie". The relevant linguistic phenomena include higher frequencies of occurrence compared with other kinds of posture verbs and differential behavior with respect to some morphosyntactic patterns involving notions such as agentivity. The posture morphemes can also be the source for a variety of semantic extensions reflecting experiential realities of the postures, such as the extension of "lie" to mean "sleep" in some languages. Extensions also include grammaticalizations of the posture morphemes to locative and aspectual markers which reflect the temporal stability and spatial fixedness of the postures themselves. This article is categorized under: Linguistics > Cognitive Linguistics Linguistics > Language in Mind and Brain.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".