The presumption of innocence and reverse onus provisions in Maltese law
Bibliographic record
Abstract
In this paper I discuss the right to be presumed innocent until proven \nguilty in the light of reverse onus provisions in Maltese and foreign \nlaw, A problematic issue which arises in several jurisdictions such \nas in the United Kingdom, Canada, South Africa and Malta, concerns \nthe assessment of reverse onus provisions in relation to the \npresumption of innocence. In order to delve deeper into this vexata \nquaestio, three provisions of the Official Secrets Act and a provision \nin the Interpretation Act of Malta are singled out with a view to \nestablishing whether they do, and, if so, to what extent do they \ncontravene the presumption of innocence rule contained in the \nConstitution of Malta and in the European Convention on Human \nRights and Fundamental Freedoms. Due consideration is also given \nto Maltese and foreign case law on the subject under discussion and \nthe relevance of such jurisprudential activity to the interpretation \nby Maltese, British, Canadian and South African courts, and the \nEuropean Commission and Court of Human Rights, of the provisions \nunder examination. In sum, it is concluded that there are indeed \nsome instances where the presumption of innocence might be \ninfringed by the provisions of the said Maltese legislation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".