SENIORS ON THE STAND: ACCOMMODATING OLDER WITNESSES IN ADVERSARIAL TRIALS
Bibliographic record
Abstract
Adversarial trials are structured on the assumption that the most reliable evidence comes through the in-person cross examination of witnesses. However, for many older adults, aging introduces physical and cognitive changes that can interfere with the ability to meet this basic requirement. This paper considers whether the legal and procedural rules that have been developed to ensure that only the most reliable evidence is used as the basis of fact finding in a trial may disproportionately be excluding evidence from seniors. To explore this tension, I identify four physical and cognitive issues that increase in prevalence with old age that can interfere with a witness’s ability to testify in person. I then review the promise and limitations of the current rules of evidence and procedure in meeting the potential challenges experienced by older witnesses. While current laws of evidence and procedure contain tools that can accommodate individuals who experience limitations, a case law review suggests that they are seldom used to facilitate the participation of older adults in trials. With the proportion of people aged over 65 expected to double in the next 20 years, it is critical for future research to explore this gap.
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 | 0.600 | 0.699 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".