Navigating Systemic Barriers: Experiences of Deaf Persons in the Criminal Justice System in Amhara National Regional State, Ethiopia
Bibliographic record
Abstract
The Constitution of the Federal Democratic Republic of Ethiopia (the FDRE Constitution) gurantees everyone suspected or accused of crime the due process rights, including the right to be informed—in a language they understand—of the reasons for their arrest, the nature of the charges, the right to challenge evidence and witnesses, and the right to legal counsel. The criminal justice system relies on spoken language and is predominantly managed by hearing professionals, raising critical concerns about the ability of deaf individuals to access and effectively navigate it. However, this issue remains underexplored. Using Constructivist Grounded Theory, this thesis examines deaf people’s experiences in the criminal justice system as victims, suspects, and accused. Interviews were conducted (n=27) with deaf persons (n = 11), sign language interpreters (n = 7), and justice sector professionals (n = 9). The legal landscape was also critically examined, and comparative insights from Canada’s jurisprudence were drawn. Four key themes emerged from the data, through the CoGT coding process, capturing the systemic barriers, discrimination, inequities, and injustices that deaf persons routinely face in their interactions with police and in court proceedings. These include communication barriers, limited professional awareness, attitudinal bias, and legal obstacles. These barriers constitute a denial of fundamental human rights and indicate that the criminal justice system in Ethiopia remains fundamentally inaccessible to deaf people. The intersection of deafness and gender exacerbates the inaccessibility of the system for deaf female survivors of sexual assault. The analysis of Canada’s jurisprudence highlight significant progress, offering valuable lessons to guide future reforms. The findings suggest urgent need for legal and institutional reforms. Given these barriers are structural, systemic change is a neccessity. Legal frameworks must be revised to ensure accessible and inclusive police and court systems. Awareness-raising and mandatory training for justice professionals are also essential. Further research and advocacy are also critically needed. Concrete steps should be taken to implement the Convention on the Rights of Persons with Disabilities (CRPD). Ethiopia should ratify the Optional Protocol to the CRPD and the Protocol to the African Charter on the Rights of Persons with Disabilities in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".