Bibliographic record
Abstract
Rebel groups are often portrayed as mere predators. Yet some groups establish governance systems that deliver public services to the population. Why would they do this? This is the question that Rebel Rulers addresses. The empirical basis for this innovative study consists of three case studies. The author looks at the governance systems set up by the Liberation Tigers of Tamil Eelam (LTTE) in Sri Lanka, the Rally for Congolese Democracy (RCD) in Congo, and the Sudan People's Liberation Movement/Army (SPLM/A). There is a puzzling variance between these three cases: the Tamil Tigers successfully set up relatively effective governance structures that provided public services to the population living in the territories controlled by the LTTE. The LTTE even managed to enrol the government of Sri Lanka in bankrolling some of these services, a fact which the author attributes to the desire of the government to maintain some ties with its population in rebel territories. By contrast, the Rwandan and Congo-backed RCD insurgency never managed to install a minimally effective governance system. The third case – the SPLM/A in Southern Sudan – holds the middle ground. The SPML/A was relatively successful in enlisting NGOs and international agencies in its attempts to develop a governance system. It managed to develop a partially effective governance system that provided a degree of stability in parts of South Sudan, but failed to meet other basic needs, such as food security or basic healthcare.
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 | 0.003 | 0.003 |
| 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 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".