Searching for stable electricity in Monrovia: Co-evolution of energy infrastructure and practices
Bibliographic record
Abstract
Development requires action, adaptation, and transformation of both governance and physical infrastructure. Energy infrastructure unfolds as the infrastructure that facilitates the growth of other infrastructures and development. This paper argues it is critical to examine the complex, non-linear evolution of energy infrastructure and policy alongside the intricate, non-linear evolution of governance in general, and planning specifically, in southern cities, particularly those with a history of instability, scarcity and incomplete infrastructure. Monrovia, Liberia, provides a compelling example of the intricate co-evolution of policy, infrastructure and practices in unpredictable and unstable contexts that require adaptability, resilience and innovation. Understanding such a landscape is important because governance reflects characteristics of constant evolution, improvisation and searching within a broader system that is also evolving. The objective is to better grasp how the double and coupled processes of ‘searching’ and ‘planning’ interact to shape the landscape of options for tackling incomplete electricity infrastructure. The incompleteness of both governance and infrastructure retains benefits whilst the interplay of searching and planning can allow for positive adaptations in terms of governance and infrastructure; however, we equally know adaptation can take place in unsustainable contexts, thereby engendering the potential for risks and missed opportunities.
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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 | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".