Bibliographic record
Abstract
Abstract The positive effects of rule of law norms and institutions are often assumed in the peacebuilding literature, with empirical work focusing more on processes of compliance with international standards in war-torn countries. Yet, this article contends that purportedly ‘good’ rule of law norms do not always deliver benign benefits but rather often have negative consequences that harm the very local constituents that peacebuilders promise to help. Specifically, the article argues that rule of law promotion in war-torn countries disproportionately favours actors who have been historically privileged by unequal socio-legal and economic structures at the expense of those whom peacebuilders claim to emancipate. By entrenching an inequitable state system which benefits those with wealth, education, and influence, rule of law institutions have reinforced structural, social, and cost-related barriers to justice. These negative effects explain why war-torn societies avoid the formal courts and law enforcement agencies despite substantial international efforts to professionalise and strengthen these institutions to meet global rule of law standards. The argument is drawn from an historical, comparative, and empirical analysis of the UK-funded justice sector development programme in Sierra Leone and US-supported rule of law reforms in Liberia – two postwar countries often cited as prototypes of successful peacebuilding.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".