Bibliographic record
Abstract
The evolution of arm-leg relationships presents something of a problem for embodied cognitive science. The affordances of habitual bipedalism and upright posture make our two sets of appendages and their interrelationships distinctively human, but these relations are largely neglected in evolutionary accounts of embodied cognition. Using a mixture of methods from historical linguistics, Cognitive Linguistics and linguistic anthropology to analyze data from languages around the world, this paper identifies a robust, dynamic set of part-whole relations that emerge across the human waistline between upper and lower appendage sets cross-culturally. The general pattern—identified as “arm-leg syncretism”—provides a plausible primary source for the uniquely human penchant for creative analogy, or “double-scope conceptual blending”, said to underlie the human language faculty (Fauconnier and Turner 2002, 2008; Deely 2002; Anttila 2003; Bybee 2010). This account not only addresses a conspicuous gap in the literature but also enables us to better understand what it means to be human—including how we came to be unique among other species and how we are still vitally interrelated with other species. Deely (2010) blends both sides of this tension into a single phrase: “the semiotic animal”. The paper further develops this distinction by drawing attention to one of the roles upright posture played in the emergence of semiotic consciousness.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".