SocialScope: Enabling Information Discovery on Social Content Sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, many content sites have started encouraging their users to engage in social activities such as adding buddies on Yahoo! Travel and sharing articles with their friends on New York Times. This has led to the emergence of social content sites, which is being facilitated by initiatives like OpenID 1 and OpenSocial 2. These community standards enable the open access to users ’ social profiles and connections by individual content sites and are bringing contentoriented sites and social networking sites ever closer. The integration of content and social information raises new challenges for information management and discovery over such sites. We propose a logical architecture, named SocialScope, consisting of three layers, for tackling the challenges. The content management layer is responsible for integrating, maintaining and physically accessing the content and social data. The information discovery layer takes care of analyzing content to derive interesting new information, and interpreting and processing the user’s information need to identify relevant information. Finally, the information presentation layer explores the discovered information and helps users better understand it in a principled way. We describe the challenges in each layer and propose solutions for some of those challenges. In particular, we propose a uniform algebraic framework, which can be leveraged to uniformly and flexibly specify many of the information discovery and analysis tasks and provide the foundation for the optimization of those tasks. 1.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it