Character perspective shift sequences and embodiment markers in signed and spoken discourse
Bibliographic record
Abstract
Abstract The aim of this study is to present a distributional portrait of forms of character-perspective sequences as produced by LSQ (Langue des signes québécoise) signers and Quebec French speakers, in relation to corporal and grammatical marking in a set of recorded discourses. Among the forms we examine are grammatical, corporal and rhythm markers. As for the types of character perspective shift examined, we focus on the nature of the event that is being enacted: speech, thought, action or gesture. The dataset employed in the study consists of short, elicited narratives using video sketches as stimuli. Both Deaf signers and French speakers were asked to describe short scenarios that were displayed without any signing or speech. Half of the stimuli were constructed from a series of factual events containing no emphatic reactions or actions, while the other half included emphatic elements. Twenty-four narratives produced by these two groups were transcribed and coded using ELAN to determine the distribution of character perspective shift sequences (CPS) used in terms of presence (duration) and frequency (occurrences). Further markers were also identified in terms of frequency, which was then analyzed with a factorial ANOVA statistical model. The overall finding of this study is that CPS is used in both language groups, despite their varying results in terms of the distribution of frequency and markers.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".