Bibliographic record
Abstract
Video media spaces are an excellent crucible for the study of privacy. Their design affords opportunities for misuse, prompts ethical questions, and engenders grave concerns from both users and nonusers. Despite considerable discussion of the privacy problems uncovered in prior work, questions remain as to how to design a privacy-preserving video media space and how to evaluate its effect on privacy. The problem is more deeply rooted than this, however. Privacy is an enormous concept from which a large vocabulary of terms emerges. Disambiguating the meanings of and relationships between these terms facilitates understanding of the link between privacy and design. In this article, we draw from resources in environmental psychology and computer-supported cooperative work (CSCW) to build a broadly and deeply rooted vocabulary for privacy. We relate the vocabulary back to the real and hard problem of designing privacy-preserving video media spaces. In doing so, we facilitate analysis of the privacy-design relationship.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".