Moving the Needle: A Qualitative Evaluation of Implementation Fidelity in Reentry Programming
Bibliographic record
Abstract
The state of... has notable rates of incarceration and probation combined, with a rate of.. per people. With …residents on probation, the state has systems of confinement within and outside of prison that compare internationally. This state holds a reputation of having the harshest mass punishment laws when it comes to systems of confinement, lead post-offending individuals to pathways back into incarceration. Various research and evaluation studies demonstrate what works when reintegrating groups from prison to community, but there is limited understanding of how reentry programming and its interventions are working and to what degree, beyond quantified success. This study explores the direct experiences of program stakeholders in four facility sites at a reentry program in... Using qualitative evaluation methods to ascertain program fidelity at each of reentry sites, I explore what program recipients and staff members at the program believe affect their environment of a successful transition. Findings implicate the need for additional training in staff members, as well as organizational and inter-organizational dialogue with diverse community member groups that allow for individual-based resource provision and community investment. The data suggests links between paraprofessional staff members’ histories in substance misuse and program participants’ feelings of trust, identity, and safety.
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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 | 0.085 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".