Bibliographic record
Abstract
Text passwords pose a number of difficulties for end users, who must create, remember, and manage large numbers of passwords.Users are often regarded as the weak link in security systems, but they are a crucial component of the system, and need to be better considered in the design of security products.Many password alternatives have been proposed, but none have successfully replaced ordinary text passwords, and the potential consequences of password problems grow as more information relating to work and life is stored online.This thesis explores practical approaches to helping users select, securely reuse, and manage passwords, and investigates questions about password alternatives.The attention is on the end user, and how authentication affects these users in their daily lives.Our focus is on practical, actionable results to assist end users in their daily tasks.I can't begin to list what I've learned from my supervisor, Robert Biddle.Robert's enthusiasm and broad knowledge are an inspiration and made working with him a pleasure.Thank you for showing me that I could combine all of my areas of interest, and believing that I could be good at this.I would also like to thank my mentor Sonia Chiasson, for her advice, friendship, career inspiration, and not least for answering all of my questions.My friends and family have provided me with so much love and support throughout my time as a student.I'd like to thank my parents for their unwavering enthusiasm, and especially thank my mum for sharing her statistics expertise and being a fantastic role model.I'd also like to thank my friend Emily Miller-Cushon for her discussion
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".