Bibliographic record
Abstract
www.scidev.net was launched last week to bridge the divide between knowledge rich developed countries and the knowledge poor developing world. Sponsored by the journals Nature and Science, the site was created on the premise that “those who stand to benefit most from modern science and technology tend to be those who have least access to information.” Over the past few years there has been increasing recognition of impact of the knowledge gap on developing countries. To this end free access to medical research published by the BMJ has been possible via bmj.com since 1995. All 23 specialist journals published by the BMJ Publishing Group are currently available free of charge to 44 low income nations and there are plans to extend this access to 34 lower middle income countries. Earlier this year six of the world's leading medical publishers signed a “statement of intent” to provide free access to scientific information for more than 100 of the poorest countries in the world (BMJ 2001;323:65). Against this background, scidev.net is now the first website dedicated to the needs of the developing world. It reports and discusses aspects of science and technology that are relevant to sustainable development and specific to the needs of developing countries. Each week up to four full length research articles from each of the journals Science and Nature are posted on the site. There is also a news section on development related scientific and policy issues, and in depth dossiers are being created on topics such as gene cloning, climate change, and malaria. The site—funded by UK, Swedish, and Canadian development agencies—also advertises job opportunities and international meetings. Links are available to funding agencies, and other development agencies. Overall the site gives the feel of being a forum where connections are made, ideas exchanged, and information shared. Together with the changes in publishing, it shows how the electronic revolution could help to abolish the information gap.
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.129 | 0.035 |
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".