Towards efficient document content sharing in social networks
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
Social network services have enabled the increasing sharing of digital content (e.g., images, videos and audios). However, despite the fact that office documents hold a significant amount of users' digital content, office documents have not yet been sufficiently exploited by social networks. The main reason for this is that existing office document architectures/formats are not open enough for selective access, reuse and commenting of document parts. As a response to this problem we have developed a new document architecture, namely the Semantic Document Architecture (SDArch), which enables the annotation of document content with semantic and social context annotation and provides easy access and reuse of the desired document parts. In this paper we focus on the social context annotation (SCA) that we have introduced to capture implicit information about the usage of document content in the context of the social network. We present a ranking algorithm that uses SCA along with user profile information, to get more personalized search results. The current version of SDArch prototype, which implements the algorithm, is also discussed.
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 | 0.000 | 0.000 |
| Open science | 0.001 | 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