Bibliographic record
Abstract
1: An introduction to the languages of urban Africa Fiona Mc Laughlin (University of Florida, USA) I: African urban languages and their histories 2: The historical dynamic of multilingualism in Accra M.E. Kropp Dakubu (University of Ghana-Legon, Ghana) 3: Urban Wolof: profile of a language Fiona Mc Laughlin (University of Florida, USA) 4: The spread of Lingala as a lingua france in the Congo basin, Eyamba G. Bokamba (University of Illinois at Urbana-Champaign, USA) II: Theoretical approaches to the study of African urban languages 5: Are African cities really different linguistically? Some insights from Cape Town, Cecile Vigouroux (Simon Fraser University, Canada) 6: Discourses, community, identity: Processes of linguistic homogenization in Bamako, Mali, Cecile Canut (CNRS-Paris, France) 7: Polarizing and blending: compatible practices in a bilingual urban community in Cape Town, Kay McCormick, (University of Cape Town, South Africa) III: The question of identity in African urban languages 8. The story of old-urban vernaculars in North Afric, Atiqa Hachimi (Atiqa Hachimi, University of Florida) 9: Language choice in Dar-es-Salaam's billboards, Charles Bwenge (University of Florida, USA) 10: The multiple facts of Abidjan's urban language form, Nouchi, Sabine Kube (UNESCO-Paris, France) 11: Multilingualism and language use in Porto-Novo, Benin Wale Adeniran (Obafemi Awolowo University, Nigeria) 12: On the linguistic vitality of Ga~ in Accra, James Essegbey (University of Florida, USA) IV: The evolution of urban languages in Africa 13: Innovations on the fringes of the Swahili-speaking world: observations from Bujumbura, Haig Der Houssikian, (University of Florida, USA) Index.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".