Stuttering and Labor Market Outcomes in the United States
Bibliographic record
Abstract
Purpose: The purpose of this study was to quantify relationships between stuttering and labor market outcomes, determine if outcomes differ by gender, and explain the earnings difference between people who stutter and people who do not stutter. Method: Survey and interview data were obtained from the National Longitudinal Study of Adolescent to Adult Health. Of the 13,564 respondents who completed 4 waves of surveys over 14 years and answered questions about stuttering, 261 people indicated that they stutter. Regression analysis, propensity score matching, and Blinder-Oaxaca decomposition were used. Results: After controlling for numerous variables related to demographics and comorbidity, the deficit in earnings associated with stuttering exceeded $7,000. Differences in observable characteristics between people who stutter and people who do not stutter (e.g., education, occupation, self-perception, hours worked) accounted for most of the earnings gap for males but relatively little for females. Females who stutter were also 23% more likely to be underemployed than females who do not stutter. Conclusions: Stuttering was associated with reduced earnings and other gender-specific disadvantages in the labor market. Preliminary evidence indicates that discrimination may have contributed to the earnings gap associated with stuttering, particularly for females.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".