Green Jobs for Returning Citizens: A Solution to the Interwoven Problems of Climate Change and Recidivism
Bibliographic record
Abstract
Climate change is dismantling social protections through both direct effects of disasters and theindirect consequences as communities attempt to recover, which often exacerbates trauma andparallel known risk factors for recidivism among returning citizens. As a discipline, social workmust wholly recognize the links between environmental injustice and other primary factors ofinequality such as race and class, which also increases the likelihood of incarceration followedby re-entry into society. Green jobs programs can allow society to address two seeminglydisparate issues: recidivism and climate change, offering practical and mutually beneficialsolutions. By building on the decarceration movement’s efforts and following examples fromLos Angeles and Chicago, the field of social work can continue to advocate for green jobs as anopportunity to lead climate change and recidivism mitigation discourse while simultaneouslyoffering solutions to the most pressing issues of our time. The authors argue that this form ofmultisolving on micro, mezzo, and macro levels is the future of the field.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".