Looking at Viewpoint in <scp>ASL</scp> Through a Cognitive Linguistics Lens
Bibliographic record
Abstract
Central to how signed languages such as American Sign Language (ASL) express the viewpoint of a signer is the space surrounding the signer's body, and primarily that in front of the signer. Perspective-taking, in its most basic form, is physical and perceptual in nature, where signers might map a scene experienced in the past onto their present surrounding space as they engage in narrative discourse. But beyond this, signers also express conceptual viewpoint in terms of how they view, subjectively, more abstract ideas, for example expressing a particular stance toward someone's actions, and space frequently plays a role here too. The expression of viewpoint affects linguistic structure in a variety of ways, for example, when the perspective shifts from one story character to another, referring to various entities must be tracked, for which ASL has particular linguistic mechanisms that signers employ. At an abstract level, ASL has certain constructions that reflect viewpoint, one example of which is topic-comment constructions, where a topic phrase is subjectively chosen (often paradigmatically) as a means of framing a state of affairs, which is one kind of conceptual viewpoint, whereas the comment that follows is a construction containing, pragmatically, the signer's belief or stance regarding that state of affairs. Through a cognitive linguistics lens, we can see how aspects of viewpoint in ASL involve instances of conceptual blends, relying on metaphor and metonymy, body partitioning, and image schemas.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".