Bibliographic record
Abstract
Interdisciplinary perspectives on the role of new information technologies, including mobile phones, wireless networks, and biometric identification, in the global refugee crisis. Today's global refugee crisis has mobilized humanitarian efforts to help those fleeing persecution and armed conflict at all stages of their journey. Aid organizations are increasingly employing new information technologies in their mission, taking advantage of proliferating mobile phones, remote sensors, wireless networks, and biometric identification systems. Digital Lifeline? examines the use of these technological innovations by the humanitarian community, exploring operations and systems that range from forecasting refugee flows to providing cellular and Internet connectivity to displaced persons. The contributors, from disciplines as diverse as international law and computer science, offer a variety of perspectives on forced migration, technical development, and user behavior, drawing on field work in countries including Jordan, Lebanon, Rwanda, Germany, Greece, the United States, and Canada. The chapters consider such topics as the use of information technology in refugee status determination; ethical and legal issues surrounding biometric technologies; information technology within organizational hierarchies; the use of technology by refugees; access issues in refugee camps; the scalability and sustainability of information technology innovations in humanitarian work; geographic information systems and spatial thinking; and the use of “big data” analytic techniques. Finally, the book identifies policy research directions, develops a unified research agenda, and offers practical suggestions for conducting displacement research. Contributors Elizabeth Belding, Karen E. Fisher, Daniel Iland, Lindsey N. Kingston, Carleen F. Maitland, Susan F. Martin, Galya Ben-Arieh Ruffer, Paul Schmitt, Lisa Singh, Brian Tomaszewski, Mariya Zheleva
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.454 | 0.247 |
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".