Case Study: Tobacco Economics Control Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Tobacco Control Economics Project is a project that seeks to gather evidence on tobacco use and economics in southern Africa. It is a project of the University of Cape Town with support from the DataFirst repository based at the University of Cape Town. Its aim is to gather data that already exists, sometimes in digital form, frequently in offline records or in some cases paper records, and bring them together as an open resource. The project faces challenges of data gathering as well as permissions. Frequently data is or should be “available” in some form but control over the data is relinquished only unreluctantly. In many cases the legal standing of data is unclear. Many of the challenges relating to the bringing together of the data involve ascertaining what the legal standing of a dataset is or gaining permissions for its re-use. DataFirst is a longstanding data sharing infrastructure with professional and experienced data management staff. Challenges of ensuring continued funding and maintenance are similar to those of data infrastructures globally. The infrastructure meets international standards and provides leadership to other services and platforms in this space.
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.
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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