Bibliographic record
Abstract
We are broadly concerned with security risks associated with the spread of misinformation. Misinformation is spread when messages are exchanged on a network, and some agents erroneously believe reports from agents that do not have suitable expertise. So the spread of misinformation is closely connected to the notion of belief change, which is a topic that has been studied extensively in the formal Artificial Intelligence (AI) community. The problem is that formal models of belief change are generally focused on fully rational agents. In practice, however, the trust that agents have in information sources is influenced by non-logical factors. For example, agents might be more likely to trust sources that have demonstrated alignment on some unrelated set of values. In this short paper, we propose a formal approach to modelling belief change that is based on a flexible kind of trust transformation, where the extent to which a source is trusted is not entirely dependent on expertise or reliability. We demonstrate how this approach can be used to capture the propagation of false facts over a social network. This is a preliminary paper outlining a basic approach, which we intend to use as the basis for software that provides logic-based recommendations related to the accuracy of information received on a social network. We see this as a kind of reasoning prosthetic, which limits the spread of information by providing transparent information that helps users understand the consequences of their own beliefs.
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.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".