What is the default behavior in accommodation: Convergence or maintenance?
Bibliographic record
Abstract
This study, a secondary analysis of data from a phonetic accommodation study, considers the default behavior of speakers in accommodating to another speaker in an interaction. Should convergence or maintenance be considered the default behavior? There is inherent acoustic variation in our speech. Every time we produce a sound, such as a voiceless stop in English, it varies along some phonetic dimension, such as voice onset time (VOT). We might expect that, in the absence of any external influence, these voiceless stops will be realized with VOT longer than their overall mean 50% of the time and with VOT shorter than their overall mean 50% of the time. During interaction with another person, however, studies in social-psychology have suggested that lack of adjustment (maintenance) may be akin to divergence [Tong et al. (1999)]. In addition, convergence is a fairly robust finding in studies of accommodation and imitation [e.g., Nielsen (2011), Babel (2012)], suggesting that perhaps the default behavior in interaction is convergence. The purpose of this talk is to introduce these points of view, discuss the factors that may affect our interpretation, and facilitate discussion on this issue, which has implications for the growing body of research investigating accommodation.
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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 | 0.007 | 0.043 |
| 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 | 0.003 |
| 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".