Examination of customized questioned digital documents
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
With the increasing trend of digitization of business processes and personal communication across the globe, digital documents of intrinsic value continue to be created. Whereas the questioned document examination (QDE) field of forensic science deals with the examination of "physical" documents potentially disputed in a court of law, there are no developed approaches for handling questioned digital documents (QDDs). Although techniques that address related problems such as identifying document types and image forensics exist, concrete strategies for analyzing questioned "digital" documents still need to be developed. This paper focuses on developing methods to examine QDDs that are customized from a database, due to the versatile use of customized documents in many areas. As a basis for our approach, we make the case for the need to develop analysis techniques for a digital counterpart of QDE which we term Questioned Digital Document Examination (QDDE). We posit that there is a benefit in considering digital aspects of forensic science disciplines where the questions answered by the discipline are clear, from a digital perspective. The paper describes some of the aspects that can be considered in the domain of question digital document examination. In designing methods for QDDE, we discuss the process of document recreation and describe the feasibility of our recreation process in different scenarios. Our experiments show that an alternative approach of considering digital aspects from a well-defined physical domain is worthwhile. It also supports the practical application of our approach in examining documents customized from a database.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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