Football and Post-War Reintegration: exploring the role of sport in DDR processes in Sierra Leone
Bibliographic record
Abstract
Growing enthusiasm for 'Sport for development and peace' (SDP) projects around the world has created a much greater interest among critical scholars seeking to interrogate potential gains, extant limitations and challenges of using sport to advance 'development' and 'peace' in Africa. Despite this interest, the role of sport in post-conflict peace building remains poorly understood. Since peace building, as a field of study, lends itself to practical approaches that seek to address underlying sources of violent conflict, it is surprising that it has neglected to take an interest in sport, especially its grassroots models. In Africa, football (soccer) in particular has a strong appeal because of its popularity and ability to mobilise individuals and communities. Through a case study on Sierra Leone, this paper focuses on sports in a particularly prominent post-civil war UN intervention—the disarmament, demobilisation and reintegration (DDR) process—to determine how ex-youth combatants, camp administrators and caregivers perceive the role and significance of sporting activities in interim care centres (ICCS) or DDR camps. It argues that sporting experiences in ddr processes are fruitful microcosms for understanding nuanced forms of violence and healing among youth combatants during their reintegration process.
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 | 0.001 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".