Rigour and Feasibility in Tobacco Control Evaluation: Toward a Successful Reconciliation
Bibliographic record
Abstract
Abstract: The challenge of reconciling scientific rigour with feasibility is central to the goals of policy and program evaluation for tobacco control. Evaluations conducted in various settings are held to high standards of performance, and must also be considered feasible by program authorities and stakeholders. This article describes three recent examples from the field of tobacco control. Issues of context, relevance, and stakeholder participation in planning evaluation designs are central to successful reconciliation. To affect tobacco control evaluation positively, reconciliation between the goals of rigour and feasibility needs to occur on two levels: between evaluator and stakeholders, and within the evaluation plan.
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 | 0.854 | 0.838 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.012 | 0.054 |
| Scholarly communication | 0.038 | 0.035 |
| Open science | 0.010 | 0.040 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".