Scholars-at-Risk: challenges facing humanity
Bibliographic record
Abstract
According to the United Nations High Commissioner for Refugees (UNHCR), the global displacement has just hit another record, as more than 100 million people were forcibly displaced worldwide by persecution, conflicts, violence or other threatening human lives’ events (e.g. climate change) in 2022. Migration of such vulnerable populations also includes scientists. Due to the ongoing brutality in their countries and/or the rising uncertainties within academic and research environments, many highly educated and skilled scholars are bound to leave their homes. These scholars have the potential to continue playing a significant role in increasing the scientific knowledge, if provided with necessary support systems in their host country. Although many International Institutions are working in this field to protect such vulnerable scientists in affected countries or support them in knowledge production in a new host country, still the fact is, many of them face multiple challenges. To understand those difficulties, a group of scientists and stakeholders including scholars-at-risk decided to highlight the most important challenges and subsequent needs those scholars have, and to probe how science diplomacy and international efforts can help saving science and scientists. Findings of this paper are based on personal interviews conducted with refugee, displaced, and at-risk scholars from several backgrounds, such as, Syria, Iraq, Yemen, Afghanistan, and Turkey. Both structured and unstructured questions were part of the interviews which were conducted via phone calls (Zoom, WhatsApp) and in-personal meetings. This publication derives several vital insights from those interviews, highlighting the diaspora journeys, challenges faced by Scholars-at-Risk, and suggesting solutions at the organizational, political and personal levels of Scholars-at-Risk. Most importantly, it could be seen that mutual understanding and feedback between sponsor organizations and displaced scholars is of utmost importance for improving efficacy and possible outcomes of all those initiatives and securing a better future for such displaced community.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.039 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".