A Typology of Privacy
Bibliographic record
Abstract
Despite the difficulty of capturing the nature and boundaries of privacy, it is important to conceptualize it. Some scholars develop unitary theories of privacy in the form of a unified conceptual core; others offer classifications of privacy that make meaningful distinctions between different types of privacy. We argue that the latter approach is underdeveloped and in need of improvement. In this paper, we propose a typology of privacy that is more systematic and comprehensive than any existing model. Our typology is developed, first, by a systematic analysis of constitutional protections of privacy in nine jurisdictions: the United States, Canada, the United Kingdom, the Netherlands, Germany, Italy, the Czech Republic, Poland, and Slovenia. This analysis yields a broad overview of the types of privacy that constitutional law seeks to protect. Second, we have studied literature from privacy scholars in the same nine jurisdictions, in order to identify the main dimensions along which privacy can be classified. Our analysis led us to structure types of privacy in a two-dimensional mode, consisting of eight basic types of privacy (bodily, intellectual, spatial, decisional, communicational, associational, proprietary, and behavioral privacy), with an overlay of a ninth type (informational privacy) that overlaps, but does not coincide, with the eight basic types. Because of the comprehensive and large-scale comparative nature of the analysis, this paper offers a fundamental contribution to the theoretical literature on privacy. Our typology can serve as an analytic and explanatory model that helps to understand what privacy is, why privacy cannot be reduced to informational privacy, how privacy relates to the right to privacy, and how the right to privacy varies, but also corresponds, across a broad range of countries.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".