How can institutions better support researchers? The case of extremism and terrorism research
Bibliographic record
Abstract
Research in certain fields of study may carry emotional and safety-related risks. For example, scholars in the field of extremism and terrorism often navigate potentially uncomfortable or unsafe environments, face an emotional toll when exposed to extreme ideologies or risk facing backlash from extremists, either during the research process or after the publication of their findings or media appearances. However, support provided for them tends to be limited, often due to the lack of institutional awareness of the risks inherent in researching potentially dangerous populations. Drawing on 13 interviews with directors and coordinators of research institutions that have developed guidelines and protocols to protect researchers, as well as 7 internal documents produced by these institutions, this article examines institutional practices for preventing, mitigating and responding to harm, threats and harassment of researchers. The findings emphasize the role of institutions in establishing a safe organizational culture, implementing safety tools and protocols and considering the intersectional nature of risks and challenges.
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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 | 0.216 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.040 | 0.110 |
| Scholarly communication | 0.072 | 0.080 |
| Open science | 0.007 | 0.052 |
| Research integrity | 0.039 | 0.033 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".