Continuous primitive accumulation in Ghana: the real-life stories of dispossessed peasants in three mining communities
Bibliographic record
Abstract
The purpose of this article is to provide an opportunity for the peasants in three mining communities in Ghana to voice their experiences of primitive accumulation under contemporary global neo-liberalism. There is a plethora of literature on the exploitation of Africa, drawing on theories of new imperialism or ‘accumulation by dispossession’. However, there is not much grassroots empirical work on how different social groups experience accumulation by dispossession. It seems that NGOs and journalists do better on this than intellectuals. I seek in this article to contribute to filling this lacuna, by focusing on the hardest hit social group, namely, the peasants. I also argue that the existence of this lacuna has major theoretical and political implications for the struggle for alternatives to capitalism and ‘development’ that is distinctly anti-imperialist. The article is based on data collected through focus group discussions and in-depth personal interviews with peasants affected by surface mining activities of transnational mining corporations in three mining communities in the resource-rich Western Region of Ghana: Prestea, Dumasi and Teberebie.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".