A demonstration of the application of the new paradigm for the evaluation of forensic evidence under conditions reflecting those of a real forensic-voice-comparison case
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
The new paradigm for the evaluation of the strength of forensic evidence includes: The use of the likelihood-ratio framework. The use of relevant data, quantitative measurements, and statistical models. Empirical testing of validity and reliability under conditions reflecting those of the case under investigation. Transparency as to decisions made and procedures employed. The present paper illustrates the use of the new paradigm to evaluate strength of evidence under conditions reflecting those of a real forensic-voice-comparison case. The offender recording was from a landline telephone system, had background office noise, and was saved in a compressed format. The suspect recording included substantial reverberation and ventilation system noise, and was saved in a different compressed format. The present paper includes descriptions of the selection of the relevant hypotheses, sampling of data from the relevant population, simulation of suspect and offender recording conditions, and acoustic measurement and statistical modelling procedures. The present paper also explores the use of different techniques to compensate for the mismatch in recording conditions. It also examines how system performance would have differed had the suspect recording been of better quality.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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