Do we become more honest as we age? A multi-methodological approach to studying dishonesty across adulthood
Bibliographic record
Abstract
Being dishonest with others is a common social behaviour, and it has been proposed that dishonesty increases throughout childhood, peaks in adolescence, and gradually declines across adulthood (i.e., an aging-honesty-effect among older adults). Yet, very little research has comprehensively explored how dishonesty is used and evaluated in later life. Using a multi-methodological approach, the primary goals of my dissertation were to examine if this aging-honesty-effect replicated across methodologies and social contexts and to provide a deeper understanding of the deceptive profiles of older adults to uncover what they lie about, who they lie to, and how they morally evaluate lies. In Study 1, I measured younger and older adults’ willingness to cheat in a spontaneous deceptive paradigm and personality traits of honesty-humility. In Study 2, younger and older adults completed an experience sampling study where they recorded their daily lies for a 7-day period. In Study 3, younger and older adults morally evaluated truths and lies, and participants were recruited in Canada, Singapore, and China to examine if age differences were culturally dependent. Results supported the proposed aging-honesty-effect where older adults were less likely to cheat in a task when given the opportunity (Study 1), they scored higher in the honesty-humility personality trait (Study 1), and they told fewer lies across a 7-day period (Study 2) compared to younger adults. Extending these results beyond lie frequency, Study 2 provided insight into the ways in which younger and older adults use lies in their natural social lives, uncovering that this aging-honesty-effect can vary depending on the type and topic of the lie and the relationship between the liar and the lie recipient. Finally, Study 3 found that not only are older adults more honest themselves, but they evaluate blunt or immodest honesty more favorably and good-intentioned lies less favorably than younger adults, and these effects persisted beyond a Western cultural context. These results provide the foundation for understanding older adults’ use and evaluation of dishonesty and can contribute to constructing a lifespan model of dishonesty from childhood through to old age.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".