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
The growth of online social networks has seen the utilisation of these network graphs for the purpose of providing recommendations. Automated recommendations, however, do not take into account inter-personal trust levels that exist in a social network. In this article, we propose a privacy-preserving trusted social feedback (TSF) scheme where users can obtain feedback on questions from their friends whom they trust. We show that the concept can be extended to the domain of crowdsourcing -- the trusted crowdsourcing (TCS) scheme. In crowdsourcing, instead of asking friends, one can solicit opinions from experts in the crowd through a privacy preserving trusted feedback mechanism. Our proposal supports categorical answers as well as single-valued numerical answers. We evaluate our proposals in a number of ways: based on a prototype implementation built atop the Google App Engine, we illustrate the performance of the trusted social feedback. In addition, we present a user study to measure the impact that our trusted social feedback proposal has on users' perception of privacy and on foreground trust. We also present another user study to capture a model for user acceptance testing of the trusted crowdsourcing.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.039 | 0.090 |
| 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