Bibliographic record
Abstract
The acceptable threshold for privacy is an individual choice, informed by culture, tradition and experience. That it is important, conversely, is self-evident. We use it to moderate personal information disclosure, how we choose to act and dress every day. However, the debate about privacy has struggled because of an incomplete scholarship that often halts with the question ???what is privacy???? Similarly, the affirmative statement ???privacy is dead??? is often made without further explanation of what we have lost. \nThis thesis provides a clarification of privacy by presenting a formal model and tool for precise discussion. It can be implemented, for example, in a mobile application or embedded on a website. The utility of the formal model is supported by survey research of professionals in the field and those with no particular related work experience. The formal model has given us several insights to how privacy behaves enabling progress towards an interdisciplinary understanding of terminology. In particular, it demonstrates and solves for the problem of transitivity in privacy because it can follow each personal information disclosure as it travels beyond the data subject through a network of people, processes and technologies.\nIn addition to the formal model and observations about the behaviour of privacy, a contribution of this thesis is its review of computer science literature specifically for contributions to privacy research, an assessment of current privacy practitioner methods, a study of privacy impact assessment practices at Ontario hospitals, and a detailed exploration of the possibilities of future work.
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 | 0.059 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".