Bibliographic record
Abstract
This dissertation was undertaken in an effort to determine the efficacy of the current legal system procedures designed to remedy the presence of bias, and specifically racial prejudice, in our jury selection process. These procedures known as the challenge for cause were systematically examined over the course of three studies. The first study conducted in this series was done in the laboratory and showed evidence that the challenge for cause is ineffective in identifying and rejecting biased jurors. The second study was an observational study conducted at the Toronto Superior Court of Justice in which the challenge for cause procedures were examined in their actual practice. During the course of this study a naturalistic variable presented itself for consideration. Specifically, it was observed that during the challenge for cause process the jury pool was either allowed to remain in court for the entire procedure or the jury pool remained outside the court and were called in one by one to be asked the challenge for cause question. The results of this study suggest that when the jury pool remains inside the court, jury pool members are less likely to admit to being prejudiced, and there are less overall rejections. The third study, also conducted in the laboratory, was done to test this naturalistic variable and revealed evidence that the presence or absence of the jury pool during the challenge for cause is an influential variable on the admission of prejudice and the rejection of potential jurors.
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 | 0.060 | 0.282 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".