An assessment of global Internet-based HIV/AIDS media coverage: implications for United Nations Programme on HIV/AIDS' Global Media HIV/AIDS Initiative
Bibliographic record
Abstract
No studies to date have assessed the quantity of HIV/AIDS-related media on the Internet. We assessed the quantity of language-specific HIV/AIDS Internet-based news coverage, and the correlation between country-specific HIV/AIDS news coverage and HIV/AIDS prevalence. Internet-based HIV/AIDS news articles were queried from Google News Archives for 168 countries, for the year 2007, in the nine most commonly spoken languages worldwide. English, French and Spanish sources had the greatest number of HIV/AIDS-related articles, representing 134,000 (0.70%), 11,200 (0.65%) and 24,300 (0.49%) of all news articles, respectively. A strong association between country-specific HIV/AIDS news coverage and HIV/AIDS prevalence was found, Spearman's rank correlation: 0.6 (P < 0.001). Among countries with elevated HIV/AIDS prevalence (> or =10%), the volume of HIV/AIDS-specific media was highest in Swaziland (15.9%) and Malawi (13.2%), and lowest in South Africa (4.8%) and Namibia (4.9%). Increased media attention should be placed on countries with high HIV/AIDS prevalence and limited HIV/AIDS-specific news coverage.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".