Bibliographic record
Abstract
Abstract A plethora of studies on terrorism underscores the challenges of managing the psychological and behavioural impacts of terrorist events. This literature also emphasizes the idea that the global pending threat of terrorism, prior to the occurrence of any event, may also give rise to significant reactions among members of the public. Drawing from the literature on proactive coping, the current study presents the results of factor analyses performed on sections of a national survey that assessed appraisals of as well as actual responses to the threat of terrorism in Canada (N=1,502). Findings revealed that items assessing individual response to terrorism were represented by three factors in this context: Individual Preparedness, Information Seeking, and Avoidance Behaviour. Further analyses demonstrated a tendency for actual preparedness behaviours to be associated with decreased psychological stress, and actual avoidance behaviours to be associated with heightened psychological stress. Furthermore, the divergent patterns of relationships of terrorism response appraisals and corresponding actual responses with psycho-logical stress emphasized the need to distinguish different stages in the process of preventive coping with terrorism. Theoretical and practical implications of findings for individual preparedness in Canada are discussed.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.478 | 0.125 |
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".