The everyday politics of waste collection practice in Addis Ababa (2003–2009)
Bibliographic record
Abstract
This article examines the unique approach the Addis Ababa City Administration put in place to address the city’s municipal solid waste collection problems between 2003 and 2009. During this period, the city administration introduced a variety of governmental technologies to discipline waste as a material and to institute government-initiated cooperative micro-enterprises as a major actor in waste collection. In this article, I examine how the variety of measures the city administration introduced during the waste management reform disciplined waste collectors. I unpack this issue through examining the specific spaces of engagement between waste collectors (formal and informal) and city administration’s representatives by paying close attention to the everyday practices of waste collection. I also examined how the emphasis on reducing unemployment over the idea of creating a clean city can be better explained as a political exercise. Primary data collected included interviews of purposely-selected experts (n=28) and waste collectors (n=42). Secondary data were also consulted. I use the concept of the everyday state and the notion of governmentality for the purpose of examining the intricate social relations that materialized between waste collectors and city administration and how this shaped waste collection spaces and practices. The findings reveal that the city administration was more focused on assisting cooperative micro-enterprises with the aim of reducing unemployment over the idea of creating a clean city. It is also shown that the different governing technologies the city administration employed to discipline waste as a material were in fact aimed at assisting cooperative micro-enterprises and reconfiguring the power relationship between waste governing institutions and waste collectors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".