Bibliographic record
Abstract
Password managers are tools that create and store passwords, and are widely viewed as a secure and convenient password management strategy.However, these tools are under-adopted.This research investigates users' mental models of password managers and aims to understand their motivations for adopting these tools.We conducted an online survey with 197 participants and an interview study with 21 participants, who were divided into three groups: users of dedicated password managers, users of browser-based managers, and non-users.Participants' password management approaches ranged from relying on default options, such as saving passwords in web browsers, to deliberate decisions to break with previous habits, such as adopting dedicated programs.Our thematic analysis showed that users' adoption of password managers is influenced by their understanding of online security.We suggest that increasing the transparency of password manager properties to users and leveraging their trust in smartphones could induce effective adoption of password managers.I also want to express my gratitude to Sonia Chiasson for her high-level expertise and thoughtful insights, which helped me broaden my understanding of the discipline.I am honored to have had the privilege of working under the supervision of the two great professors who have enriched my academic experience.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".