Bibliographic record
Abstract
substantial geopolitical, economic, and health burden (Perlman et al., 2011;Rubin et al., 2007).Terrorism is known to invoke feelings of dread and fear among the public, although its specific form can vary depending on region, political climate, and legal context.Terrorism is primarily a criminal act that is politically motivated (Bhui et al., 2016).Research in the field leaped to the forefront following the 9/11 terrorist attacks in 2001.Although terrorism has been linked to a host of socio-economic, political, ideological, psycho-social, and religious factors, no universal terrorist profile has been identified; nor is there a consensus definition for terrorism (Christmann, 2012;Desmarais et al., 2017; McGilloway et al., 2015).Like any human behaviour, the act of terrorism is also influenced by a complex interplay of individual and environmental factors that work gradually over time, culminating into a trigger point.Although the science of risk assessment has allowed mental health professionals to make informed predictions about the risk of future violence, predicting radicalization to terrorism remains a challenge.Notwithstanding, many legal jurisdictions call on the expertise of psychiatrists, particularly forensic psychiatrists, to evaluate individuals involved in terrorism, likely due to the perception that terrorism is underpinned by mental health issues.Therefore, it is imperative that the psychiatric evaluation of those involved in terrorism be supported by a thorough understanding of the psycho-social, socio-demographic, cultural, medicolegal, and ethical aspects of terrorism.This is particularly important in situations where no specific training is provided on the evaluation of those involved in terrorism, for instance, as part of a forensic psychiatry fellowship program.This issue of the International Journal of Risk and Recovery presents three of four papers that delve into key issues on the categorization of terrorism, psychiatric assessment of individuals involved in terrorism, ethical issues in the
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".