Liar liar! Pants on fire: Detecting the trustworthiness of children's statements
Bibliographic record
Abstract
Interpersonal trust is essential to our social relations and is vital for maintaining positive interpersonal relations, both in terms of friendship formation and maintenance and in terms of conventions of day-to-day communications (Grice, 1980; Rotenberg, 1991; Rotter, 1980). Honesty is an integral part of trustworthiness. According to Rotenberg and colleagues' conceptualization of trustworthiness, honesty is one of three bases of trust (Rotenberg, Boulton, and Fox, 2005; Rotenberg, Fox, Green, Ruderman, Slater, Stevens, and Carlo, 2005; Rotenberg, MacDonald, and King, 2004). According to this framework, there are three fundamental bases of trustworthiness that include honesty (which is the focus of this chapter), reliability, and emotional trust (see Chapter 2 for further details). Others' perceptions of one's honesty are also an important aspect of trustworthiness, as they can affect the assessment of one's trustworthiness and have social consequences. Individuals hold cognitive representations of the extent to which they trust another (i.e., belief that another is telling the truth). Thus, an adult may believe that a child is honest. However, there is also the actual behavior of the child, which is their dispositional trustworthiness . A child's dispositional trustworthiness is reflected in their behavior to tell the truth and keep promises. Thus, there is a dyadic relationship between both trust beliefs and the trustworthiness revealed by the child's behavior. A dyadic partner holds trust beliefs that may match (or mismatch) the trustworthiness of another.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".