Bibliographic record
Abstract
In legal argumentation it is important to be able to ascertain the strength of the arguments that are presented by the legal parties or by the judge.It is clear that the argumentative force of each single argument in multiple argumentation is stronger than in coordinative compound argumentation.Within coordinative compound argumentation however, the arguments need not to of the same importance.In this paper I will discuss suggestions as to how the argumentative force of arguments may differ and how these differences could be reconstructed. 1. C.My client is convinced that the vendor was aware of the fact that there was water under the house. J.How can he be so sure about that?C.The vendor was aware of the fact that there had been water under the houses of both neighbours for quite some time. J.That doesn't mean very much.I understand that there is water under more houses in that particular neighbourhood.C. The vendor's house, however, is known to have an underground connection with its neighbours.C. My client is convinced that the vendor knew that there was water under the house. J.How can he be so sure about that?C. According to the plumber, the vendor had 1000 litres of water pumped away from the crawl space of his house only last year. J.Most experts do agree that 1000 litres of water in the crawl space of a house is indeed alarming, others hold that only an amount of at least 1200 litres results in an alarming situation.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".