Zones of Exclusion: Offshore Extraction, the Contestation of Space and Physical Displacement in the Nigerian Delta and the Mexican Gulf
Bibliographic record
Abstract
Abstract: This article examines two aid interventions that manifest the merging of community development/relief and industrial security policy in the petroleum offshore of the Nigerian Niger Delta and the Mexican Gulf. In the Nigerian case, the article considers the crisis in the Warri region of Delta State in 2003, the subsequent evacuation of local residents, and the surrounding context of oil‐related violence. Simmering since the 1990s, the 2003 Warri conflict displaced thousands due to competing community claims to territory that “hosts” oil installations, Shell and Chevron primarily. In Mexico, the analysis centers on the implementation of 2003 Mexican security legislation, prompted by International Maritime Organization post 9/11 security policy, that amplifies the “Zone of Exclusion” around offshore installations. To offset the loss of livelihoods resulting from the “exclusion zone”, Mexican state agencies offered financing to support the conversion of the displaced small‐scale fishers to fish farming. The varying forms of displacement prompted by these two “liberating” interventions reflect the socio‐historical specificity of territorial relations in the Nigerian and Mexican extractive regimes. These relations constitute divergent extractive settings which have come to play contrasting roles in the global political economy of oil, one highly volatile, the other relatively stable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".