It Takes a Village to Reduce Recidivism: Examing Ex-Offenders DEI & Belonging in Higher Education
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. Objective #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'. Objective #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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".