Interviewing activists and terrorists: a detailed research protocol
Bibliographic record
Abstract
In the domain of PVE as well as reintegration, the most interesting studies are arguably based on material collected first-hand from the individuals involved in the phenomenon of political violence or terrorism. As more individuals from the 2013-2016 wave of foreign terrorist fighters are exiting the criminal justice system, young individuals with no memory of that period are sympathizing with ISIS and others again are joining right-wing groups with violent agendas. Understanding the motives behind such engagement will always lead a portion of the scholars to pursue interview-based studies. This paper describes the research protocol used for a study which dealt with politico-ideological mobilization and violence in relation to causes and conflicts in the Arab World. More than one hundred interviews were conducted in Lebanon, Switzerland and Canada with individuals involved in politico-ideological mobilization or violence of different ideological orientations. Besides interviews, complementary material in the form of ethnographic fieldnotes and voice recordings via instant messaging were collected. The data was compiled into a MAXQDA database and coded according to the principles of Grounded Theory, using open, selective, axial and theoretical coding. The paper further discusses epistemological and ethical considerations.
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 | 0.093 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.058 | 0.028 |
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".