Topicality and the problem of “topic” in American Sign Language discourse
Bibliographic record
Abstract
American Sign Language (ASL) has been characterized as a topic-comment or as a topic-prominent language. But studies based on actual usage reveal a great deal of variation, leading to the proposal here that the very idea of a definable category of topic should be re-examined. Janzen (2007) proposes a topicality hierarchy for constructions in ASL, finding that the higher degree of topicality an expression has, the less likely it would be found as a marked topic. Only expressions at the lowest levels of the topicality scale occur as marked topics in topic-comment constructions. When the topic content is nominal, marked topic phrases are typically explicit noun phrases or full clauses. Relatedly, marked topics indicate topic shift rather than topic maintenance. In line with Langacker’s (2008, 2013) analysis of topic expressions as reference-point constructions, topics in ASL are subjectively chosen by signers as a pragmatic framing mechanism, given that the signer might have multiple viable reference points from which to choose. It is also an intersubjective choice, taking into account assumptions regarding the addressee’s knowledge store. If “topic” is a language-specific notion, characterizing topic as a cross-linguistic category may be problematic. It is within this context that “topic” is explored in this study of ASL.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".