Bibliographic record
Abstract
Abstract Recent work has shown that ASL (American Sign Language) signers not only articulate the language in the space in front of and around them, they interact with that space bodily, such that those interactions are frequently viewpointed. At a basic level, signers use their bodies to depict the actions of characters, either themselves or others, in narrative retelling. These viewpointed instances seem to reflect “embodied cognition”, in that our construal of reality is largely due to the nature of our bodies ( Evans and Green, 2006 ) and “embodied language” such that the symbols we use to communicate are “grounded in recurring patterns of bodily experience” ( Gibbs, 2017 : 450). But what about speakers of a spoken language such as English? While we know that meaning and structure for any language, whether spoken or signed, affect and are affected by the embodied mind (note that the bulk of research on embodied language has been about spoken, not signed, language), we can learn much about embodied cognition and viewpointed space when spoken languages are treated as multimodal. Here, we compare signed ASL and spoken, multimodal English discourse to examine whether the two languages incorporate viewpointed space in similar or different ways.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".