The Intertwined Relationship between Power and Patriarchy: Examples from Resource Extractive Industries
Bibliographic record
Abstract
This study examines the relationships between extractive industries, power and patriarchy, raising attention to the negative social and environmental impacts these relationships have had on communities globally. Wealth accumulation, gender and environment inequality have occurred for decades or more as a result of patriarchal structures, controlled by the few in power. The multiple indirect ways these concepts have evolved to function in modern day societies further complicates attempts to resolve them and transform the social and natural world towards a more sustainable model. Partly relying on queer ecology, this paper opens space for uncovering some hidden mechanisms of asserting power and patriarchal methods of domination in resource-extractive industries and impacted populations. I hypothesize that patriarchy and gender inequality have a substantial impact on power relations and control of resources, in particular within the energy industry. Based on examples from the literature used to illustrate these processes, patriarchy-imposed gender relations are embedded in communities with large resource extraction industries and have a substantial impact on power relations, especially relative to wealth accumulation. The paper ends with a call for researchers to consider these issues more deeply and conceptually in the development of case studies and empirical analysis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".