Bibliographic record
Abstract
This thesis addresses two problems of trust: \n \n1. Knowledge on Trust: If we are provided with information by a variety of individuals, whom we trust to different degrees, what is the best overall theory we can form from the information we are given? \n \n2. Social Trust: If one does not have direct experience of an individual, how can one establish an initial degree of trust through the offices of society? \n \nIt addresses the first problem by developing a formal, mathematical and computational, model of Bonjour’s Coherence Theory of Knowledge, and the second by adapting abstract argumentation theory to reason about networks of relationships of trust and distrust between individuals. In developing the latter it develops a notion of generalised argumentation systems, giving their semantics via the Galois Connections induced by binary relations, and provides a general scheme of evaluation of these systems based on propositional model finding. \nThroughout, some effort is made to set the work in the context of both theories of trust and of the day-to-day trust situations that one encounters in everyday life.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".