Design Patterns for Multiple Stakeholders in Social Computing
Bibliographic record
Abstract
In social computing, multiple users may have privacy stakes in a content (e.g., a tagged photo). They may all want to have a say on the choice of access control policy for protecting that content. The study of protection schemes for multiple stakeholders in social computing has captured the imagination of researchers, and general-purpose schemes for reconciling the differences of privacy stakeholders have been proposed. A challenge of existing multiple-stakeholder schemes is that they can be very complex. In this work, we consider the possibility of simplification in special cases. If we focus on specific instances of multiple stakeholders, are there simpler design of access control schemes? We identify two design patterns for handling a significant family of multiple-stakeholder scenarios. We discuss efficient implementation techniques that solely rely on standard SQL technology. We also identify scenarios in which general-purpose multiple-stakeholder schemes are necessary. We believe that future work on multiple stakeholders should focus on these scenarios.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".