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
We begin an investigation into the semantic patterns underlying user choice in passwords. Understanding semantic patterns provides insight into how people choose passwords, which in turn can be used to inform usable password policies and password guidelines. As semantic patterns are difficult to recognize automatically, we turn to visualization to aid in their discovery. We focus on dates in passwords, designing an interactive visualization for their detailed analysis, and using it to explore the RockYou dataset of over 32 million passwords. Our visualization enabled us to analyze the dataset in many dimensions, including the relationship between dates and their co-occurring text. We use our observations from the visualization to guide further analysis, leading to our findings that nearly 5% of passwords in the RockYou dataset represent pure dates (either purely numerical or mixed alphanumeric representations) and the presence of many patterns within the dates that people choose (such as repetition, the first days of the month, recent years, and holidays).
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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