Dutch Jihadists: An exploratory study on the changing motivations of Dutch jihadists leaving for Syria and Iraq
Bibliographic record
Abstract
Since the end of 2012, Dutch nationals have been traced to travel to Syria and Iraq to join ISIS.This study aims to explore the highs and lows in the departing numbers per quarter, starting in 2011 with the outbreak of the Syrian civil war and ending in the second quarter of 2017.To explain the fluctuations in these numbers, this thesis looks at the interplay between the motivations of these foreign fighters and the conflicts' developments in Syria and Iraq.Motivations to foreign fight can be approached from different levels and angles, all influencing the decision to foreign fight in different manners, which is also reflected in the theories of Malet, Bjørgo and Venhaus on foreign fighters.This, together with a discussion of foreign fighter involvement in previous conflicts and the motivations of foreign fighters in general to join ISIS will be highlighted in the second part.In the third part, the conflict developments and the motivations of Dutch foreign fighters joining ISIS are highlighted.In the analysis of this thesis, the different components will be combined in order to determine the changes in motivations and their effect on the departing number of foreign fighters.This analysis showed, that there is a pattern in the motivations of Dutch foreign fighters and the developments within the conflicts.In the beginning of the conflict, humanitarian reasons are the main driver behind mobilization.This shifts into personal and ideological reasons after the establishment of the Caliphate and during the end of the analysed period, the use of violence is the main motivation to join.The interplay between conflict developments and motivations comes forward in the periods were high numbers of foreign fighters leave, however this interplay is found to a lesser extent with regard to the low numbers in departure.8
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".