Self-fulfilling prophecies: the government's role in generating support for ethnic terrorists
Bibliographic record
Abstract
Why do some ethnic groups support the use of terrorism and violence to change the status quo and others do not? Conventional wisdom suggests repression may provoke more violence, but consensus has not been reached on how this relationship works. I propose a Theory of Ethnic Group Support for Terrorism (TEST) that argues that the denial of political access to ethnic groups to resolve conflict through peaceful means creates conditions of structural repression. This structural repression then engenders ethnic groups to mobilize around seeking extra-institutional and often extra-legal avenues of change including terrorism. The avenue chosen depends upon the state's response to ethnic group mobilization, which may include reforming repressive institutions, thus undermining support for violence and terrorism, or may include using agent-driven discrete acts of repression, triggering increases in ethnic group support for terrorism. The TEST is applied and assessed in light of the cases of Northern Irish Catholics and the IRA, Quebecois and the FLQ and Corsicans and the FLNC. Ultimately, the TEST demonstrates that states' willingness to effectively address ethnic group grievances will marginalize, if not eradicate terrorism. In contrast, states that choose to neglect, alienate and discriminate against ethnic groups fail to enable them to use political channels to change the status quo, causing increases in support for terrorism.
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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 | 0.001 | 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 | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".