Militant Islamists or borderland dissidents? An exploration into the Allied Democratic Forces' recruitment practices and constitution
Bibliographic record
Abstract
Abstract Descriptions of the Allied Democratic Forces (ADF) – a relatively resilient rebel group in the Congo–Uganda borderland – are almost solely focused upon the rebellion's Islamic extremist characteristics. Through looking specifically at the ADF's recruitment practices, this paper seeks to problematise existing accounts of the group's constitution. It discusses how Islamic extremism has had a significant influence on various aspects of ADF recruitment, and therefore helps to explain particular dimensions of the ADF's composition. Nevertheless, this paper demonstrates that focusing on the role of Islamismalone, leaves a large part of the ADF's story untold – such as the important role played by recruitment networks associated with marginalised and militarised ex-combatants, or the populations of disenfranchised youth in the borderlands. Indeed, unresolved political and socio-economic injustices amongst the people of the Rwenzories have been just as significant motivating factors for joining the rebels as have Islamic sources. Thus, this paper argues that the ADF's recruitment practices and constitution cannot be sufficiently analysed without adequate recognition of the rebel group's position within a borderland context.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".