Professionalising the science of Digital Forensics - Policy Logging and audit-able record keeping as a Life-long record
Bibliographic record
Abstract
This paper proposes that there is the potential to create the means by which Investigators might enhance the professionalism of their work, their employability (in certain circumstances) and create a framework within which their robust evidential recovery might be complemented. The concept of extending the current practise of incident and investigation record keeping to something akin to an Aeronautical Pilots Logbook: i.e. a record of all matters to do with their flying history. In respect of how that translates in the Digital Investigation realm, a comprehensive record of all elements that might constitute an individual’s record of work and development. Although the natural environment of debating note taking for digital forensic evidential purposes is legal in nature, the origins of this paper relate less to Law than the processes involved in investigation and are a good deal more fundamental than examining forensic artefacts. They also reflect the concepts of professionalising practise and establishing an understanding of what constitutes evidential sufficiency for the purposes of court proceedings, but how this process can be applied to individual development and progress. By taking the rationale that governs the investigative note taking employed by Senior Investigating Officers (SIOs), investigating the most serious and complex of criminal offences there is an opportunity to enhance current contemporaneous note taking and keeping. The object in so doing is twofold: codify current practices and give them a framework that is consistent and well understood; and secondly, to establish the recorded decision making processes to make the auditing of an investigation a much more transparent, obvious and sequential process. Inevitably, there is a need to consider the legal aspects of how note taking has relevance to proceedings and whilst the paper refers to Association of Chief Officers (ACPO) Principles regarding Digital Evidence, the universal application of these concepts should be understood.
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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 | 0.038 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".