Bibliographic record
Abstract
Recent events such as the terrorist attack in Algeria (January 2013), the Boston Marathon Bombing (April 2013), the apprehension of two suspected al-Qaeda linked terrorists in Toronto, (April 2013), highlight the requirement for greater understanding regarding the radicalization and recruitment of terrorists. As detailed in the US Department of State Report (2011), over 10,000 terrorist attacks occurred in 2011, affecting nearly 45,000 victims in 70 countries and resulting in over 12,500 deaths. With a focus on the outcomes and results of terrorist activities, terrorism itself often becomes a ‘blackbox' concept that does not capture the essence of the radicalization process nor the mechanisms of recruitment. This conceptual paper introduces Actor Network Theory (ANT) as a systems lens to open the ‘blackbox' of terrorism. This systems lens ‘…is a discipline for seeing wholes. It is a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static snapshots' (Senge, 1990:68). The systems view facilitated by ANT is supported and informed by methods of network analysis and conceptual modelling that highlight how dynamic networked actors shape the radicalization process through the actor network process of translation.
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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".