Towards Automated Deduction in Blackmail Case Analysis with Forensic Lucid.
Bibliographic record
Abstract
This work-in-progress focuses on the refinement of application of the intensional logic to cyberforensic analysis and its benefits are compared with the finite-state automata approach. This work extends the use of the scientific intensional programming paradigm onto modeling and implementation of a cyberforensics investigation process with the backtrace of event reconstruction, modeling the evidence as multidimensional hierarchical contexts, and proving or disproving the claims with it in the intensional manner of evaluation. This is a practical, context-aware improvement over the finite state automata (FSA) approach we have seen in the related works. As a base implementation language model we use in this approach is a new dialect of the Lucid programming language, that we call Forensic Lucid and in this paper we focus on defining hierarchical contexts based on the intensional logic for the evaluation of cyberforensic expressions.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".