Time Is of the Essence: Temporality and Competition as Drivers of Terrorist Credit-Taking
Bibliographic record
Abstract
Despite widespread focus on the communicative function of terrorism, terrorists frequently forgo claiming responsibility for their attacks. So why don’t terrorists claim their attacks? The scholars who have attempted to answer this question have primarily focused on group- and target-based differences. I propose an alternative theory, emphasizing the importance of temporality. Intuitively, the passing of time following group entry should change the utility of verbal claims of responsibility as an emerging group cannot rely on a previously established reputation. Levels of terrorist competition over time further influence verbal credit-taking-especially when competitors share attack styles-leading to decreased credit-taking over time in terrorist monopolies relative to competitive settings. I explore these dynamics through case-studies of Canada and Ireland during the 1960–1970s. Canada poses a monopoly case, whereas the Irish case experienced extensive intra- and inter-group competition. Coding newspaper articles, I appended the Canadian Incident Database with a measure of verbal credit-taking and juxtaposed it with Domestic Terrorist Victims data. Results show the expected newcomer dynamic, backing a temporal theory, yet neither intra- nor inter-group competition showed any effect despite the most-likely nature of the cases.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".