Beyond primary victims: child victims of terrorism, and the role of terrorism media
Bibliographic record
Abstract
Terrorism is defined as a use of force by individuals or groups, which is directed towards innocent civilians and, using tactics which instill fear and terror, is intended to influence or force changes in political or social decisions and policies (Marsella & Moghaddam, as cited in Government of Canada Department of Justice, 2015;Slone & Shoshani, 2008).The individual characteristics of the victims who are on site during the terrorist attack are not as important as the scale of possible calamity.The goal of a terrorist attack is not to target a specific group of people (Slone & Shoshani, 2008), but to advance a politically-driven message.This is why, most often, highly populated areas such as clubs or busses are the targets of terrorist acts, and these are areas most often frequented by students, women, and passersby, who make up the most common demographic categories of victims (Canetti-Nisim, Mesch, & Pedahzur, 2006).Acts of terrorism can be considered a form of "interpersonal victimization," and have the potential to produce trauma in those exposed.Such trauma may be the result of the interaction between various interpersonal factors (i.e. a sense of betrayal, injustice, malevolence, etc.; Pereda, 2013).The majority of terrorism victims are considered "indirect victims," or those whose victimization is a result of exposure other than being on the site of the terrorist attack (Slone & Shoshani, 2008).This is in contrast to "primary victims," those present at the scene and time of a terrorist attack (Fischer et al., 2011).The purpose of this paper is to discuss two aspects of terrorism literature that pertain to indirect victimization: media influence, and child victims of terrorism.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".