It Takes a Village to Reduce Recidivism: Examing Ex-Offenders DEI & Belonging in Higher Education
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
Objective #1: Highlight the benefits of DEI and belonging in Higher Education for previously incarcerated individuals: Introduce Case study- Michelle Jones and Harvard university: From Prison to Ph.D. Counterfactual thinking is a concept that involves the human tendency to create alternatives to life events that have already occurred; something that is contrary to what happened. “If she hadn’t committed the crime would be a sure candidate.” Linking Recidivism to the lack of education: 48% of incarcerated people who participate in higher education opportunities are less likely to recidivate than those who do not. \nObjective #2: Present Research and Theories of Recidivism to advance the normalcy of DEI& B for ex-offenders in Higher Education settings: Labeling Theory- Marxists effectively developed labelling theory so it would recognize the social and political structures in which labels are created and adhered to classify people. General Systems Theory (GST)- It is argued that general systems theory (GST) reveals important insights into criminal justice structures and functions. Specifically, it is argued that the criminal justice system processes “cases” rather than people. There are four basic elements to the systems model: output, process, input, and feedback. Goffman’s Stigma Theory (GST)- A Canadian sociologist Erving Goffman, the term 'stigma' describes the 'situation of the individual who is disqualified from full social acceptance'. \nObjective #3: Interactive discussion activity: Hearing from the village Gathering solutions to DEI&B in Higher Ed. Split audience into 4 groups using the Random Sampling method to advise the previously incarcerated individual looking to pursue higher education. Understanding social cubism- examining 6 sides of the issue to find a resolution. (Example: I am Financial Support: I have a Pell Grant available starting July 1, 2023) University Support Community Support State Support Federal Support.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it