Don’t Put all your NGOs in the Same Basket: Investigating the Role of NGOs in Implementing Reintegration Policy for Older Adults in Conflict with the Law
Bibliographic record
Abstract
Western industrialized countries are experiencing an increase in the portion of their populations which are 65 and older. One result of this demographic shift, which is still occurring, is the increasing number of individuals in prisons and jails who are older adults. In Canada, up to 25 % of the federal prison population is considered older. Current studies focus on prison infrastructures and routines which are inadequate for older adults, with many authors suggesting that community placements are more appropriate for this population. Studies on prisoner re-entry focus on the success and use of programs for employment and substance use disorders. This study focuses on an understudied group: older adults under carceral supervision who are provided reintegration services in the community. Grounded in the social construction of target populations framework – alongside NGO research on social constructions, advocacy and resource dependency – this project asks: in a context of the devolution of reintegration policy, what explains how re-entry NGOs act to broaden the bounds of reintegration for older adults? This research uses a four-case comparison of NGOs in Montréal, Toronto, San Francisco and Houston to assess how social constructions of older adults, combined with NGOs’ strategic action through advocacy and resource diversification impacts the bounds of reintegration policy. Newspaper thematic analysis, statistical analysis of funding streams and semi-structured interviews were conducted to compare reintegration services across NGOs. Compared to the two American NGOs, Canadian NGOs provided a greater number and range of services although there was important similarity between the Toronto and San Francisco cases. Within countries the more liberal-leaning cities, with the higher frequency of positive portrayals of aging were associated with more generous bounds of reintegration policy. The findings of this project highlight the importance of considering NGO service providers as agentic rather than passive recipients of correctional department funding. NGOs deploy social constructions, strategic funding searches and advocate to achieve their organizational goals.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".