Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it