Introduction to Basic Concepts and Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A presumption is a device used in the law of evidence to enable a proposition to be taken into account as a piece of evidence in a case even though the argument supporting that proposition is not strong enough for it to meet a required burden of proof. From this definition of what presumption is, we can already see that presumption is linked to burden of proof in evidential reasoning in law. Burden of proof sets a standard for what is to be considered a proof in evidential reasoning in law. It is a device used to make it possible for a trial to arrive at a decision for one side or another in a contested case, even though all the facts of the case may not be known, and for various reasons may never be known. For example, in a criminal case, there may have been no witnesses to the crime, and the crime may have happened a long time ago. Most of the existing evidence may have been lost or destroyed. Therefore, evidential reasoning in law has to be able to move forward to a conclusion under conditions of uncertainty, lack of knowledge and even inconsistency. Typically, for example, in a trial there will be witnesses for one side, but there will also be conflicting testimony on the other side brought in by witnesses who say the opposite thing. What these conditions imply is that in a trial it is rarely if ever possible to prove or disprove the ultimate conclusion beyond all doubt. Hence, the device of having a burden of proof is necessary for the trial to reach a conclusion for one side or the other.
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it