Situating privacy online. Complex perceptions and everyday practices
Bibliographic record
Abstract
Media and research reports point to the issue of privacy as the key to \nunderstanding online behaviors and experiences. However, it is well recognized \nwithin privacy advocacy circles that ?privacy? is a loose concept encompassing a \nvariety of meanings. In this paper we view privacy as mediating between \nindividuals and their online activities and not standing above them; as being \nconstantly redefined in actual practice. It is necessary to ask, therefore, what \nindividuals are reacting to when asked about online privacy and how it affects their \nonline experience. This paper is based on data generated in the Everyday Internet \nstudy, a neighborhood based ethnographic project being conducted in Toronto, \nCanada that investigates how people integrate online services in their daily lives. \nWe further propose that there are three organizing ?moments? of online privacy \nperceptions: the moment of sitting in front of the computer, the moment of the \ninteractions with it, and the moment after the data has been released in \n?cyberspace?. We argue that while the third has been given much media coverage, \nmainly through surveillance?Big Brother?reports and stories the other two \nmoments have not been sufficiently researched. This may be crippling the \nformulation of effective privacy principles and practices by policy makers and the \npublic.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".