Using conversation policies to solve problems of ambiguity in argumentation and artificial intelligence
Bibliographic record
Abstract
This investigation joins recent research on problems with ambiguity in two fields, argumentation and computing. In argumentation, there is a concern with fallacies arising from ambiguity, including equivocation and amphiboly. In computing, the development of agent communication languages is based on conversation policies that make it possible to have information exchanges on the internet, as well as other forms of dialogue like persuasion and negotiation, in which ambiguity is a problem. Because it is not possible to sharply differentiate between problems arising from ambiguity and those arising from vagueness, obscurity and indeterminacy, some study of the latter is included. The semantic web is based on what are called ontologies, or systems of classification of concepts, shown to be useful tools for dealing with these problems.
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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 | 0.029 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| 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".