Bibliographic record
Abstract
We investigate the relationship between personality types and the strength of created and\nselected passwords. For this purpose, we conducted an experiment on Amazon???s Mechanical\nTurk, with 510 participants. Participants were given a pre-questionnaire that included,\namong others, three binary questions: ???Password Awareness???, ???Security Training??? and\n???Account Hijacking???, which were used to predict participants??? exposure to passwords in\nthe past. Our results suggest that participants with higher levels of Extroversion, tend to\ncreate stronger passwords, if they were not required to change an online account password\nin the past (e.g., due to a security incident). In contrast, participants with lower levels of\nExtroversion tend to create stronger passwords (though not significantly), if they had been\nrequired to change an online account password in the past. These results indicate that there\nis a distinct relationship between the Extroversion personality dimension and the way we\ncreate passwords, whether it be in a familiar situation or not. Though password strength, as\ninvestigated, is the criterion of the aforementioned tests, it is worth mentioning that Extroversion\ncannot be deemed a predictor in this domain. We also investigated the relationship\nbetween personality and several password characteristics such as the total length, letters,\ndigits, and symbols used within a password. To this end, we note that for participants who\nhave had to change an online account password for the first time, Extroversion was directly\ncorrelated with creating and selecting shorter passwords, Openness was directly correlated\nwith creating passwords containing fewer letters, but more numbers and symbols, and Conscientiousness was directly correlated with creating passwords containing fewer symbols.\nThese results conclude that there is a distinct correlation between the construction of passwords\nand personality when participants are required to change an online account password\nfor the first time. This thesis presents the detailed observations and findings from our experiment,\ndiscuss potential considerations for contradictions, and identify related future\nresearch.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 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 teacher head, 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".