Looking at Viewpoint in <scp>ASL</scp> Through a Cognitive Linguistics Lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it