Signs of the Times: Discourse Ecologies and Street Life on Oxford St., Accra
Bibliographic record
Abstract
Abstract What happens when we stop seeing streets merely as geographical locations and rather interpret them as archives? What if, in focusing on an African street such as Oxford Street in Accra, we interpret this archive not as static, but as providing a transcript of dynamic transformations of discourse ecologies? The elaboration of a method for understanding the African street as an archive of discourse ecologies will be the main subject of this paper, with a particular focus on cell phone advertising on the street from 2006–2007. I do not stop at an examination of cell phone advertizing billboards but relate these to the veritable galaxy of other cultural inscriptions to be seen in mottoes and slogans on lorries, cars, pushcarts and other mobile surfaces that can be encountered on the street. Such mobile slogans are a distinctive feature of Accra and of many African urban environments. The central mark of these mottoes and slogans is an improvisational character that is specifically tied to the local cultural mediations that have historically been drawn upon for them. Taken together the two dimensions of inscription—billboard and slogans—hint at the arc of urban social histories, while also invoking a rich and intricate relationship between tradition and modernity, religion and secularity as well as local and transnational circuits of images and ideas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".