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
This study examines overconfidence in phishing email detection. Researchers believe that overconfidence (i.e., where one’s judgmental confidence exceeds one’s actual performance in decision making) can lead to one’s adopting risky behavior in uncertain situations. This study focuses on what leads to overconfidence in phishing detection. We performed a survey experiment with 600 subjects to collect empirical data for the study. In the experiment, each subject judged a set of randomly selected phishing emails and authentic business emails. Specifically, we examined two metrics of overconfidence (i.e., overprecision and overestimation). Results show that cognitive effort decreased overconfidence, while variability in attention allocation, dispositional optimism, and familiarity with the business entities in the emails all increased overconfidence in phishing email detection. The effect of perceived self-efficacy of detecting phishing emails on overconfidence was marginal. In addition, all confidence beliefs poorly predicted detection accuracy and poorly explained its variance, which highlights the issue of relying on them to guide one’s behavior in detecting phishing. We discuss mechanisms to reduce overconfidence.
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 | 0.005 | 0.007 |
| 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 | 0.005 |
| 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