Bibliographic record
Abstract
This thesis explores how cultural factors impact password-sharing attitudes of Bangladeshi people.We first proposed "Emics-Etics for Usable Security" framework to incorporate cultural factors in security design.We then conducted a literature review that laid a foundation for applying an Emics approach (culturally specific) to address password-sharing in Bangladesh.To understand password-sharing in Bangladesh, we followed the Emics approach and Grounded Theory method to conduct and analyze interviews of 25 Bangladeshi participants.We found four cultural forces (gender, religion, social norms, and political context) that impact password sharing.We then present our interview-data based password-sharing model that identifies connections between perceived identity and stages of password-sharing, and describe the tensions that arise.Somayaji.Thank you for your feedback and suggestions, which helped me to make my thesis stronger.A special thanks to baby Paul, for being there and supporting me silently the entire time (Thank you, Elizabeth!).Thank you, not-baby Sylvia and Dr. David Barrera for being parts of this journey
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".