The visual signature of non-understanding: A systematic replication of McDonough, Trofimovich, Lu, and Abashidze (2019)
Bibliographic record
Abstract
Abstract This replication study seeks to extend the generalizability of an exploratory study (McDonough et al., 2019) that identified holds (i.e., temporary cessation of dynamic movement by the listener) as a reliable visual cue of non-understanding. Conversations between second language (L2) English speakers in the Corpus of English as a Lingua Franca Interaction (CELFI; McDonough & Trofimovich, 2019) with non-understanding episodes (e.g., pardon?, what?, sorry?) were sampled and compared with understanding episodes (i.e., follow-up questions). External raters (N = 90) assessed the listener's comprehension under three rating conditions: +face/+voice, −face/+voice, and +face/−voice. The association between non-understanding and holds in McDonough et al. (2019) was confirmed. Although raters distinguished reliably between understanding and non-understanding episodes, they were not sensitive to facial expressions when judging listener comprehension. The initial and replication findings suggest that holds remain a promising visual signature of non-understanding that can be explored in future theoretically- and pedagogically-oriented contexts.
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.
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 | 0.022 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".