Justice-Involved Veterans & Social Work: A Resource Dependence Theory Perspective
Bibliographic record
Abstract
The salience of justice-involved military veterans endures as a pervasive social problem in the United States of America (USA). Since the 1980’s the percentages of Justice-Involved Veterans (JIV) have varied from a reduction in Vietnam to increasing numbers of Global War on Terror (GWOT) veterans (Bureau of Justice Statistics Report, 2015). In response, there has been a proliferation of magistrate diversion, correctional specialty units, Veterans Treatment Courts (VTC) and programming for JIV. Much of the progress is due to concerted identification and organizational sharing of resources. The USA Department of Veterans Affairs (VA), courts, corrections and non-profit organizations (NPO) provide a valuable service to our military men and women to remediate the JIV needs. Social work plays at critical in practice areas at the penal, VA and NPO systems in the USA. The JIV population are examined through the lens of social work practice, resource dependence theory (RDT), case example and future direction. The examination highlights the importance of internal and external resources and partnerships to meet organizational goals and to remediate JIV psycho-social problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".