(Non-) Involvement in Terrorist Violence Dataset (NITV)
Bibliographic record
Abstract
This dataset describes 206 individuals who radicalised to extremism. Exactly half of the sample (N=103) radicalised to right-wing extremism, and half to jihadism. The sample is also split 50/50 in terms of the outcome of these radicalization processes: 103 individuals became involved in the planning, preparation or execution of terrorist attacks, the other 103 did not. The purpose of our dataset was to gain insights into what variables influence these outcome differences. To that end, we used a codebook to look at structural, group and individual-level variables theorised to influence the onset and outcome of radicalization processes. The dataset describes individuals from Europe and North-America (Canada / US) with an average date of birth of 1980. Data on our population was gathered from a range of sources, such as secondary literature (e.g. academic publications, think-tank reports), journalistic accounts, court records, (auto)biographies and, where possible, privileged information drawn from semi-structured interviews and material provided by the Dutch public prosecution service. To ensure their privacy and security, all personally-identifying information has been rigorously removed. This means that no data on names, exact dates of birth, places of residence etc. is included in the dataset. All interviewees were asked to sign consent forms and the project went through formal ethics approval by Leiden University's Faculty of Governance and Global Affairs (FGGA) Ethics Committee (ref: 2019-012-ISGA-Schuurman) before it commenced.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.030 |
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".