Bibliographic record
Abstract
METHOD T wo of passing sentence is when the denounced man (or woman) is found, after a careful examination of the merits of the proceedings with a good panel of experts, to have a bad reputation for such heresy in some village, city or province, that is, when this denounced person is not convicted by his own confession or by evidence of the deed or by the lawful production of witnesses, and there are no | indications proven against him at all except precisely this bad reputation, so that no act of sorcery in particular is proven to have been committed. Such an act can serve as proof in a situation of vehement or violent suspicion, when the person threateningly uttered words about inflicting harm, saying in meaning or sense, “Soon you see what things will happen to you,” and later some effect ensued in terms of harm to bodies or to domestic animals. Therefore, in the case of someone against whom nothing is proven except precisely the bad reputation, the following procedure is to be followed. In such a situation, the sentence that can be passed for the denounced person is not one absolving him, as was discussed on the topic of Method One, but one imposing canonical purgation on him. Therefore, the bishop (or his official) or the judge should first note that in a case of heresy, it makes no difference that someone should have a bad reputation only among good men and serious persons, and instead, attention is paid in this case to his having a bad reputation among any base and simple folk. The reason is that since someone can in fact have a bad reputation among those by whom he can be accused on a charge of heresy, and a heretic can be accused by any persons at all (only mortal enemies are excluded, as was explained above |), a person can have a bad reputation among those people.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.075 | 0.026 |
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".