Bibliographic record
Abstract
Abstract As a social action, requesting confirmation involves presenting a proposition to be (dis)confirmed and seeking another’s (dis)confirmation of the proposition. This article provides an overview of the lexico-syntactic and prosodic resources used by participants to perform requests for confirmation (RfCs) and to respond to RfCs in Mandarin face-to-face interactions. Drawing on statistical results of the frequencies of a variety of linguistic resources in RfC sequences, this study shows that declaratives are the most frequently used syntactic forms for RfCs in the Mandarin data. Tags, such as shiba ‘right?’, are also frequently used by the speaker to seek (dis)confirmation from the recipient. The RfCs in the data also exhibit one prominent prosodic pattern. That is, a larger number of RfC turns in Mandarin end with falling pitch movement with very moderate slope from mid (M) to low (L). This prosodic pattern stems from the interplay between tones and intonation in Mandarin. In the responses to RfCs, a majority of them are confirmations. Also, response tokens, such as dui ‘right’ and en ‘en’ with falling intonation, are used highly frequently in responses to RfCs in the Mandarin data. Findings in this study afford cross-linguistic research on RfC sequences.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".