ETHICALITY AND THE EXPERT WITNESS: REMEMBERING WHAT HANGS IN THE BALANCE.
Bibliographic record
Abstract
The motivation to write this article was born out of a keynote lecture given by the author at the University of Windsor, Ontario, Canada for the "Trends in Forensic Sciences: CSI Windsor 2014" Conference on March 21, 2014. The thesis of the ensuing discussion, as the article's title implies, emphasizes how absolutely essential it is for a forensic expert to embody the highest standards of honesty and integrity when called upon to testify in a court of law. First principles are re-visited and discussed in order that the reader understands the necessity of being familiar with the basic rules that are applicable to expert testimony as well as the moral codes that support the doctrinal underpinnings. In addition, the passage of time is explored against a backdrop of scientific advances to help illustrate why the scientific community must remain alive to the possibility that what is seen as a present day given, may be shown not to be so in the foreseeable future. From both a scientific and a legal perspective, at a time when wrongful convictions are being uncovered with some regularity, the various issues surrounding the giving of expert evidence are considered under a much more focused and critical lens. Ultimately, recommendations are made to ensure that the ethicality of the expert witness remains intact, including methods to deter those whose own moral compass is simply not sufficient to stay the course. The tension that often arises between law and science is duly recognized, with compromises considered where appropriate. All comments found herein are solely those of the author made in his personal capacity.
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 | 0.042 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.042 | 0.043 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".