Centering the Korle Lagoon: exploring blue political ecologies of E-Waste in Ghana
Why this work is in the frame
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Bibliographic record
Abstract
Among emerging studies of the global political economy and ecology of electronic waste (or e-waste), few directly explore the already complex waste trades and materialities in relation to the general political ecology of water, flood control, dredging, and neoliberal ecological restoration. Even fewer focus on how this political-ecological challenge is unfolding in a West African context where ocean-based e-waste trades have played a dominant role. This article engages this particular domain of blue economic critique by focusing on Ghana in general and what we shall call "blue political ecologies of e-waste" in particular. The article focuses on e-waste politics unfolding in and around the Korle Lagoon in Accra, Ghana. The Korle Lagoon is an urban marine space of intensive land use, toxic waste disposal, social life, and urban ecological restoration. Amidst heavy contamination, there are attempts to rehabilitate the lagoon through the Korle Lagoon Ecological Restoration Project, an ecological science and restoration project focused on the Lagoon and its river system in the metropolitan area of Accra. It showcases the neoliberal complexities of ecological restoration. Importantly, situated in a multi-use marine environment, the project also highlights, we argue, a political ecological moment that is both about things 'blue', like water quality concerns, but also about other things non-blue such as contestation over land and housing, 'green' international NGO intervention on e-waste risk mitigation, and desires for new urban ecologies. Drawing on ethnographic research conducted between 2015 and 2018, this article contributes to blue political-ecological research and critique in Africa by asking: how do e-waste politics leak into discussions of the blue economy along the Korle Lagoon in Ghana? What are the promises and prospects of a blue political ecology of e-waste in general, and in Africa in particular?
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it