Is the Truth in Your Words? Distinguishing Children’s Deceptive and Truthful Statements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Children’s ( N = 48) and adults’ ( N = 28) truthful and deceptive statements were compared using a linguistics-based computer software program. Children (4 to 7 years of age) and adults (18 to 25 years of age) participated in a mock courtroom experiment, in which they were asked to recount either a true or fabricated event. Testimonies were then analyzed using Linguistic Inquiry and Word Count Software (LIWC; Pennebaker et al. 2007). This software has been previously used to detect adults’ deceptive statements (e.g., Bond and Lee, 2005). To date, no research has used this method on children’s narratives, nor has this software been used to compare those narratives to adult counterparts. Markers generated through the LIWC program achieved detection rates of 72.40% for samples of both children’s and adults’ narratives combined. In contrast, adult laypersons’ ( N = 48) detection rates, for the same narratives (i.e., both children and adults) were close to chance. More specifically, detection rates were above chance for truth (65.00%) and below chance for lies (45.00%). Thus, the linguistic profile provided through LIWC yielded greater accuracy for evaluating the veracity of children’s and adults’ narratives compared to adult laypersons’ detection accuracy.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it