Too Busy to Be Manipulated: How Multitasking with Technology Improves Deception Detection in Collaborative Teamwork
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
Deception is an unfortunate staple in group work. Guarding against team members’ deceptive tactics and alternative agendas is difficult and may seem even more difficult in technology-driven business environments that have made multitasking during teamwork increasingly commonplace. This research develops a foundation for a nuanced theoretical understanding of deception detection under these conditions. The intersection of information technology multitasking and deception detection theories is shown to produce various and sometimes competing ideas about how this type of multitasking might affect truthfulness assessments in real-time teamwork. A laboratory study involving a collaborative game helped evaluate the different ideas using manipulated deception and multitasking behaviors in a real-time, virtual group environment. The results provide evidence that information multitasking can actually improve deception detection, likely because multitaskers engage less in the team conversation, making themselves less manipulable. As understanding of multitasking benefits increases, managers and designers can incorporate effective multitasking into collaborative processes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it