Bibliographic record
Abstract
The resource cursenotion continues to dominate intellectual and policy analyses of the social impacts of oil, and thus frames the democratic prospects of petro-states. Within this framing, corruption, conflict and autocracy dominate representations of Africa and the paradox of its resources. The oil curse notion assumes that social impacts of oil as occur after oil has been turned into profits, rents and political power. But what if we thought about oil as a set of material flows and mobilities through space? How would this conceptualization change representations of Africa? As part of my doctoral dissertation, Spaces of Oil Flows will examine the political, cultural and social relations that oil infrastructure constitute in spaces of oil flows. Focusing on oil transportation infrastructure, and drawing from infrastructural studies, I will argue that oil pipelines and road networks are not inert technical systems, but sociotechnical processes that produce relations of inclusion and exclusion, access and disconnections, hubs and nodes, along the spaces of their flows. By focusing on the materiality of oil, my paper will open up new ways of thinking about the complexity of oil beyond the limits of the resource curse, and therefore new ways of representing Africa.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".