Security movements in extractive spaces: Dispossession, community-level grievance and resource conflicts in Ghana
Bibliographic record
Abstract
The African extractive sector is increasingly marked by grievance, conflict, and emerging security challenges. Focused on Ghana, this paper delves into community-level grievances and the concomitant security movements within extractive environments. Its central objective is to critically analyze these novel security movements as integral components of natural resource governance in Ghana's mining industry, elucidating their role in exacerbating or mitigating community-level grievances and dispossession. Additionally, it investigates the influence of security-related policies on other community grievances and security movements within natural resource governance. Our investigation reveals that the militarized policy approach to mineral extraction governance triggers new forms of security movements against mining activities. Secondly, we established that the criminalization of galamsey - illegal mining - breeds significant tension and grievance between citizens and the central government. Thirdly, mining companies’ failure to fulfill compensation and benefits agreements creates animosity, which results in violent confrontations with local communities. Finally, as a result, community members resort to resistance as a counter-hegemonic project against the adverse effects of mining activities and ill-willed government policies to manage mineral extraction. The paper, therefore, sheds light on these ‘new form security movements and their implications for community-level conflicts and grievances in Ghana's mining sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".