Description of the African Cigarette Prices Project Data
Bibliographic record
Abstract
The African Cigarette Price Project is a project that collects tobacco prices from African countries. Amongst other things, the data enable users to estimate price differences across brands, urban/rural divides, types of packaging, retail types, and trends in price over time. A total of 215 354 individual prices were collected during the first twelve rounds of the project (collected biannually from 2016 to 2022). Data collection continues to date. Data have been collected from 19 African countries, with most data from South Africa, Zimbabwe, Lesotho, Namibia and Botswana. Other countries include Ethiopia, Malawi, Tanzania, Chad, Eswatini, Mozambique, Nigeria, Zambia, Ghana, Madagascar, Kenya, Mauritius, Uganda and Cameroon. The project employs a novel data collection approach, by contracting local and international University of Cape Town (UCT) students as fieldworkers to collect price data while at home over the long university vacation. The data were collected at the retail level; the lowest level of geographic detail available in the public use dataset is the suburb. While the price data are not nationally representative, the data collection method is simple and affordable and provides an indication of the range of prices and the brands available in the respective countries. While cigarette prices make up the bulk of the data, other common tobacco products included are hookah tobacco, snuff, pipe tobacco, cigars, e-cigarettes, hand-rolled tobacco, and others. The collection of these other tobacco products started in round 4 (2017).
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.104 | 0.039 |
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".