Self‐reported inner speech use in university students
Bibliographic record
Abstract
Summary Remarkably little is known regarding what people talk to themselves about (inner speech use) in their everyday lives. Existing self‐directed speech measures (e.g., thought sampling and questionnaires) either uniquely capture inner speech frequency and neglect its content or classify self‐reported thoughts instances in overly simplistic categories determined by the researchers. In the current study, we describe an open‐format thought listing procedure as well as a refined coding scheme and present detailed inner speech content self‐generated by 76 university students. The most frequently self‐reported inner speech activities were self‐regulation (e.g., planning and problem solving), self‐reflection (e.g., emotions, self‐motivation, appearance, behavior/performance, and autobiography), critical thinking (e.g., evaluating, judging, and criticizing), people in general, education, and current events. Inner speech occurred most commonly while studying and driving. These results are consistent with the self‐regulatory and self‐referential functions of inner speech often emphasized in the literature. Future research avenues using the open‐format inner speech listing procedure and coding scheme are proposed.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".