Social-legal discourse in adults with and without traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To characterise social-legal discourse in adults with and without traumatic brain injury (TBI). METHODS: Participants, 19 adults with TBI and 21 uninjured comparison peers, completed a legal knowledge interview to discuss knowledge of laws and legal systems. Dependent variables were microlinguistic and macrolinguistic features of participants' spoken discourse. RESULTS: Participants in the TBI group produced more microlinguistic errors, t(38) = -3.07, adjusted P < 0.05, ηp2 = 0.20, and a higher rate of errors, t(38) = -3.08, adjusted P < 0.05, ηp2 = 0.20, than participants in the comparison group. Participants in the TBI group also produced more macrolinguistic errors, t(38) = -2.86, adjusted P < 0.05, ηp2 = 0.18, and a higher rate of errors t(38) = -3.94, adjusted P < 0.05, ηp2 = 0.29, than participants in the comparison group. Two cognitive-communication mechanisms, working memory and processing speed, partially explained micro- and macrolinguistic discourse features. CONCLUSION: Adults with moderate-to-severe TBI produced social-legal discourse of poorer micro- and macrolinguistic quality than their uninjured peers. Discourse quality was explained in part by working memory and processing speed. Results identify risks of TBI-related communication deficits in legal contexts and support further study of effects of TBI on intersections with legal systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".