The social dynamics of secrecy: Rethinking information and privacy through Georg Simmel
Bibliographic record
Abstract
This article argues that Georg Simmel’s ideas on secrecy can shed new light on current debates around the relevance or otherwise of privacy as a protection against surveillance interventions. It suggests an interactional approach to privacy, and considers it as a dynamic process which redefines the boundary between what information should be disclosed and what information should be concealed in every social interaction. Simmel argues that this “natural” process relies on the identification of the interlocutor: her psychological/emotional involvement in the relationship, her social position in society and the representation of her expectations. Recent empirical examples show that this interactional perspective may have the potential to reconcile differing privacy accounts, by linking theoretically different levels that are factually distinct: privacy as a collective fact, as a contextual integrity, and as an individual fact.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".