Bibliographic record
Abstract
Many data sharing systems are open to arbitrary users on the Internet, who are independent and self-interested agents.Therefore, in addition to traditional design goals such as technical performance, data sharing systems should be designed to best support the strategic interactions of these agents.Our research hypothesis is that designs that maximize the participants' autonomy can produce useful data sharing systems.We apply this design principle to both the system architecture and the functional design of a data sharing system, and study the resulting class of systems, which we call Decentralized Social Data Sharing ((DS) 2 ) systems.We formally define this class of systems and provide a reference implementation and an example application: a distributed wiki system called P2Pedia.P2Pedia implements a decentralized collaboration model, where the users are not required to reach a consensus, and instead benefit from being exposed to multiple viewpoints.We demonstrate the value of this collaboration model through an extensive user study.Allowing the users to autonomously control their data prevents the system architecture from being optimized for efficient query processing.We show that Regular Path Queries, a useful class of graph queries, can still be processed on the shared data: although in the worst case such queries are intractable, we propose a cost estimation technique to identify tractable queries from partial knowledge of the data.Through simulation, we also show that the users' control over network connections First of all, I would like to acknowledge the valuable guidance of my thesis supervisor Dr. Babak Esfandiari, who helped me follow proper research methods while allowing me a significant amount of what I should call academic freedom.This freedom is one of the reasons why this thesis took so long, but I'm not sure I could have worked this hard on something I hadn't largely chosen myself.Dr. Esfandiari was also instrumental in connecting me with useful elements such as funding and valuable aspects of my academic training, including peer reviewing activities, a teaching contract, and an industrial internship.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".