Labour Migrants and Access to Justice in Contemporary Qatar
Bibliographic record
Abstract
In 2012, the Open Society Institute’s International Migration Initiative launched a study to examine migrants‘ access to justice in Qatar. This study was led by researchers Andrew Gardner (University of Puget Sound), Silvia Pessoa (Carnegie Mellon University in Qatar), and Laura Harkness. The study was built on the foundation of a the research team’s large, three-year research project funded by the Qatar National Research Fund (QNRF). That project administered Qatar’s first large-scale survey devoted solely to exploring the migration experience. Of the 1189 migrants surveyed for that project, the research team was able to identify those individuals who had reported some interaction with Qatar’s justice system during their time on the peninsula. For the Open Society Institute project, entitled Labor Migrants and Access to Justice in Contemporary Qatar, the research team began by arranging follow-up interviews with those labor migrants who had reported interaction with the justice system in the survey. The pool of interviewees was further expanded to include domestic workers (or “housemaids”), as well as a variety of experts, legal consultants, and community leaders with an understanding of the processes and challenges labor migrants face in Qatar justice system. The research team’s goal was threefold: to provide an overview of the aspects of Qatar’s migration system that produce injustices and a summary of the problems that typically arise in migrants’ labor relations; to collate the experiences of migrants in the state-sponsored system designed to evaluate and adjudicate migrant grievances; and building upon the experiences and challenges faced by transnational laborers immersed in that justice system, to propose a set of policy recommendations that might incrementally improve labor migrants’ access to justice in Qatar. This report describes the research team's findings.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".