Bibliographic record
Abstract
We trust that you will enjoy this issue of the International Journal of Speech, Language and the Law which reports new findings on topics such as juror comprehension, witness credibility and equality in multilingual jurisdictions, presents a Ph.D. abstract on threatening communication and sadly brings the obituary of our esteemed and much-missed forensic phonetician colleague, Hermann Künzel.In addition to the above contributions, we are pleased to introduce a new category of submission to IJSLL, the 'Professional Practice Report' .The first example of this contribution type is presented by David van der Vloed and Tina Cambier-Langeveld of the Netherlands Forensic Institute (NFI), and is titled 'How we use Automatic Speaker Comparison in forensic practice' .This report provides the text of the NFI information sheet that is attached to forensic case reports where Automatic Speaker Comparison (ASC) has been used.The intended audience of the information sheet is the client: it describes how ASC is used by the NFI in casework, including the minimum requirements for the speech materials, the system used, calculation of likelihood ratios, validation of results and how ASC results are interpreted in combination with auditory-acoustic findings.Preceding the information sheet, the authors present an explanation of how the NFI procedures were developed and tested prior to their implementation, and a discussion of their legal reception.This Professional Practice Report is a welcome contribution to the field, and as editors we would like to encourage more forensic practitioners to offer transparent accounts of their procedures to be shared in this way through IJSLL.
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.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.054 | 0.055 |
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".