Bridging the gap between laws and implementation: A case study of female prisons in Ghana
Bibliographic record
Abstract
This research concentrates on the problems incarcerated females and their children face in Ghana. Researchers and human rights activists have identified some primary concerns about the infringement of rights of incarcerated women across the globe including Ghana. Their challenges include sexual abuse, childcare, gynaecological problems, food insecurity, sex trade by vulnerable groups for protection, violence, sleepless night, transmission of tuberculosis and human immunodeficiency virus, and poor healthcare. Children of incarcerated women also encounter severe problems like poor academic performance; victimization, trauma, teasing, and stigmatization from peers; high drop-out rates; exhibition of internalizing and externalizing behaviors; somatic problems; and juvenile delinquency which takes them to prisons in their adult lives. But the specific reasons why these problems continue to exist, leading to human rights abuses of inmates, have not yet been answered among researchers in Ghana, and this research set to fill that gap. Lax implementation of prison laws is one of the causes of these woes. And to draw the attention of stakeholders to these problems, the research adopted a doctrinal analysis of both primary and secondary resources related to the topic. Consequently, the normative analysis, interpretations, argumentations, and explanations of the materials, portray that, imprisonment is not serving its purpose and that, community service order is the best alternative, particularly for women offenders as the Bangkok Rules stipulate. Ghana does not implement some of the domestic and international human rights laws adopted, signed, and ratified, hence, the rampant human rights infringements in the prisons. Among the reasons found are financial constraints and lack of political will to fight corruption; political interferences in the prison service that frustrate their progress; sheer disregard for children and women’s rights and vulnerability; and inadequate street naming and citizens’ personal and bio data. To help solve these problems, best practices that have helped reduce recidivism and brought humane treatment to women offenders in Canada and Kenya have been cited for Ghana to emulate accordingly.Keywords: Implementation of laws, Human Rights, Ghana Prisons Service, Incarcerated women, Non-custodial sentencing/Community service order, Bangkok Rules
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".