Social Judgments of Digitally Manipulated Stuttered Speech: An Evaluation of Self-disclosure on Cognition
Bibliographic record
Abstract
Purpose: People who stutter (PWS) make up a small portion of the population but are susceptible to discrimination because of negative judgments made by people who do not stutter (PWNS) (Byrd, McGill, Gkalitsiou, & Cappellini, 2017; Johnson, 2008; Li, Arnold, & Beste-Guldborg, 2016). The current study sought to delve deeper into underlying cognitive processes associated with decision-making during a categorization task to further understand how PWNS respond to disfluent speech. Method: A computer mouse tracking paradigm was used to evaluate categorical judgements (intelligent vs. unintelligent) about PWS. Computer mouse trajectories have been shown to reveal underlying cognitive pull associated with competing category competition, which was used as a proxy for implicit bias (i.e., non-conscious stereotypes; Blair, 2002; Rudman, 2004) (McKinstry, Dale, & Spivey, 2008). Participants were asked to make an explicit categorization of the speaker as either intelligent or unintelligent before listening to the speaker self-disclose. Upon listening to self-disclosure, implicit bias was measured through analysis of mouse cursor trajectories. Results: Results indicated that participants had a higher proportion of intelligent responses in the Disclosure condition, showing that that participants began to update prior expectations about disfluent speech after listening to a PWS self-disclose, however, results also indicated that implicit bias was less evident in the Disclosure condition. Conclusions: These findings were interesting because not only do they add to the literature about social categorization of PWS, but the results also fill gaps by providing insight regarding how PWNSs’ cognitive systems are impacted after learning new information about disfluent speech.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".