Experimental Research on Recidivism Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The regression model and random forest model have been used to research recidivism prediction. The purpose of the research is firstly, to determine the crucial factors influencing recidivism rates among gender, ethnicity, prior crime record, misdemeanor status, and age. The second purpose is to discuss potential bias among the raw data. The third one is determining the pros and cons of machine decisions. With the work of data processing, analysis, and modeling, we had come to our result that factor terms that have a significant impact on the recidivism rate include: gender, priors, priors interact with age range, gender interact with age range. Through a variety of modeling methods, we established logistic regression models and random forest models to predict the crime rate through variables such as gender, age, and so on. Both models show decent accuracy, and two different models can be adapted to different situations. Some limitations have been admitted after comparison to other papers, such as the usage of only static variables, and at the same time, potential future improvements regarding the work are proposed in accordance with the limitations found.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it