Bibliographic record
Abstract
Smith and Petticrew succinctly outline the challenges we face in evaluating the impact of public health interventions tackling social determinants: challenges driven by multiple agencies with varying and sometimes conflicting interests, complex causal pathways and outcomes that extend beyond health. Looking ahead they call for a new evaluation approach, one that focuses on the whole before it focuses on any single part: a macro- rather than a micro-approach. They generously invite comment, which is valuable as their conclusions are important and bear repetition and re-emphasis. Some readers may take issue with points they make along the way. Does the contemporary agenda on socio-economic factors really represent a move away from infectious disease when prevalence of diseases such as tuberculosis remains one of the most blatant indicators of a breakdown in social and economic structure?1 Is the interest in social determinants really so recent or have the authors overlooked the efforts of Farr, or Virchow and others who followed who saw poverty and its associated social and political conditions as the root cause of ill health and political reform, economic development and education as legitimate instruments of public health?2
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.064 | 0.055 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".