Modeling trustworthiness of peer advice in a framework for presenting Web objects that supports peer commenta.
Bibliographic record
Abstract
In this paper, we present an approach aimed at enabling users to enrich their experience with web-based objects (texts or videos). In particular, we consider a social network of users offering commentary on the web objects they have experienced together with opinions on the value of this commentary being registered by peers. Within this framework, we integrate a reasoner that personalizes the presentation of these annotations to each new user, selectively limiting what is displayed to promote the commentary that will lead to the most effective knowledge gains, based on a modeling of the trustworthiness of the annotator and the similarity of peers who have found this commentary to be useful. We demonstrate the effectiveness of our approach for selective presentation of these web document annotations by constructing a simulation of knowledge gains achieved by users. Our method is shown to approach the ideal knowledge gains achieved by an optimal algorithm, far outpacing a system where a random selection of commentary is offered (as might match what users would experience if employing self-directed limiting of browsing behaviour). As a result, we offer an effective method for enhancing the experiences of users in contexts with potentially massive amounts of peer commentary.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".