Shared spaces, shared mind: Connecting past and present viewpoints in American Sign Language narratives
Bibliographic record
Abstract
Abstract In American Sign Language (ASL) narratives, signers map conceptualized spaces onto actual spaces around them that can reflect physical, conceptual, and metaphorical relations among entities. Because verb tenses are not attested in ASL, a question arises: How does a signer distinguish utterances about past events from utterances within a present conversational context? In narratives, the story-teller’s past-event utterances move the story along; accompanying these will often be subjective comments on the story, evaluative statements, and the like, that are geared, in the present, to the conversational partner. This usage-based study looks at how the ASL signer integrates past and present spaces in a narrative and specifically, integrates the viewpoints associated with each. Blending past and present spaces, while a conceptual notion, is in ASL played out in utterance structure and also in the fact that signed language articulation takes place in a three-dimensional space upon which both the signer and addressee have embodied, perspectivized views. Past and present conceptual spaces both occupy the physical space of articulation, and so the blends are at once conceptual and perceivable.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".