Bibliographic record
Abstract
At the start of 2014, more people were displaced globally by conflict and human rights violations than at any time since the Second World War. Although many of those displaced, from countries such as Syria, Iraq, Colombia, Kenya, and Sudan, have survived grave human rights abuses that demand redress, the links between forced migration, justice, and reconciliation have historically received little attention. This collection addresses the roles of various actors including governments, UN agencies, NGOs, and displaced persons themselves, raising complex questions about accountability for past injustices and how to support reconciliation in communities shaped by exile. Forced Migration, Reconciliation, and Justice draws on a variety of disciplinary perspectives including political science, law, anthropology, and social work. The chapters range from case studies in countries such as Bosnia, Cambodia, Lebanon, Turkey, East Timor, Kenya, and Canada, to macro-level analyses of trends, interconnections, and theoretical dilemmas. Furthermore, the authors explore the contribution of trials and truth commissions, as well as the role of religious practices, oral history, theatre, and social interactions in addressing justice and reconciliation issues in affected communities. In doing so, they provide fresh insight into emerging debates at the centre of forced migration and transitional justice. Exploring critical issues in political science and development studies, this provocative collaboration unites leading researchers, policymakers, human rights advocates, and aid workers to examine the theoretical and practical relationships between displacement, transitional justice, and reconciliation. [book abstract - no separate chapter abstract is available].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.049 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".