Bibliographic record
Abstract
Users are able to remember their phone numbers and postal codes, their student numbers, PIN numbers, and social insurance numbers. Why, then, do users have trouble remembering their passwords? This paper considers the hypothesis that being able to access written notes when needed would eventually help users to memorize the password. Further we hypothesize that writing down passwords encourages the use of passwords that are more complex than their unwritten (memorized) counterparts. We surveyed 31 participants on their opinions and experiences with writing down passwords and tested whether these participants created more complex passwords when they were encouraged to write them down. Finally, we observed whether written passwords had higher login success rates when tested again at least one week later. Results indicate that regardless of the experimental condition, users preferred to memorize their passwords than to take the extra step of referring to their written notes. Additionally, memorized and written passwords were remembered equally well. Finally, we found that users who had difficulty logging in had passwords with significantly higher mean entropy, which confirms the heuristic that complex passwords are harder to remember. We also unexpectedly found that users password habits are so strongly ingrained that they often ignored our instructions about writing or memorizing their password and continued to use their preestablished strategy. This observation is noteworthy for anyone conducting user authentication research.
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.
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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".