Effects on Remorse of Death Row Inmates
Bibliographic record
Abstract
The purpose of this study was to find a model capable of predicting the remorse felt by a death row inmate prior to their execution. The original dataset, compiled by Nguyen, My Khe (2017) from the Texas Department of Criminal Justice, consisted of 21 different variables, of which race, native county, previous crime, time spent in prison, number of victims, and percent of female victims were focussed on as predictors. The presence of apology in the final statement of the offenders (a binary measure) was used as the response variable. The chosen variables underwent several tests in SPSS including crosstabs to assess the association between the predictors and response. Several logistic regression models were fitted and compared to evaluate the influence of the predictors on the response variable; this process included both forward selection and backward elimination procedures. Finally, measures such as Cook’s Distance, Leverage and DFBetas were calculated to identify the existence of influential observations. Ultimately, the results were inconclusive. The final fitted model, which included race, time spent in prison, an interaction between race and time spent in prison, and number of victims, was questionable and could not be recommended for predictive purposes. Faculty Mentor: Karen Buro Department: Statistics
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".