Oil, power and social differentiation: A political ecology of hydrocarbon extraction in Ghana
Bibliographic record
Abstract
While there is scholarship focused on the nexus between resource extraction and development, further examination is needed of how the harms and benefits of extraction are differentiated among different stakeholders based on factors such as their access to power, authority over decision-making, social status,and gender. This article combines theoretical insights from assemblage thinking and political ecology to unpack the intertwined range of actors, networks, and structures of power that inform the differentiated benefits and harms of hydrocarbon extraction in Ghana. The study shows that power serves as a crucial ingredient in understanding relations among social groups, including purported beneficiaries of extractive activities, and other actors that constitute the networked hydrocarbon industry. The different levels (i.e. global, national, sub-national,local) at which the socio-ecological 'goods' and 'bads' of hydrocarbon extraction become manifest are relational. The article contributes to ongoing scholarly and policy discussions around extractivism by showing how a multi-scalar analysis reveals a more complex picture of the distributional politics, power asymmetries, and injustices that underpin resource extraction.
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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 | 0.002 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".