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Record W7139324420

It Takes a Village to Reduce Recidivism: Examing Ex-Offenders DEI & Belonging in Higher Education

2023· article· W7139324420 on OpenAlexaboutno aff
Teshara Arthur

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2023
Typearticle
Language
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRecidivismCounterfactual thinkingCriminal justiceHigher educationPrisonPoliticsStigma (botany)Economic Justice
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.002
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.080
GPT teacher head0.303
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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