Measuring the Diffusion of the Millennium Development Goals across Major Print Media and Academic Outlets
Bibliographic record
Abstract
Abstract To what extent did the Millennium Development Goals (MDGs) succeed in becoming a reference point for public debates, academic inquiry, and policy‐focused research? We explore this by considering three empirical questions. First, were there discernible trends in the extent of media references to the MDGs – by year, publication, and geography over the relevant period? Second, were there discernible trends in MDG references across a sample of relevant academic journals and disciplines? Third, how does the pattern of MDG media references compare to the emerging early pattern of Sustainable Development Goal (SDG) media references? In our sample, we find that newspapers in the UK, India and Nigeria had much more frequent MDG references than those published in Australia, Canada or the United States. We also find that The Lancet had a notably high frequency of MDG‐referencing articles, potentially helping to explain the distinctive patterns of acceleration on health MDGs. We further find that UN summits were a key driver of MDG coverage, with 2005 as the year of peak MDG attention. News coverage for the SDGs in 2016 was similar to latter year coverage of the MDGs, although considerably higher than related coverage in 2001 and 2002.
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".