Health biotechnology publishing takes-off in developing countries
Bibliographic record
Abstract
To gain insights into the potentials and characteristics of health biotechnology in developing countries, we carried out an analysis of health biotechnology publications in developing nations that have had some successes in this field. We analysed the patterns of health biotechnology publications of authors from seven developing countries from 1991 to 2002. Our results showed a significant growth in health biotechnology publications in developing countries. Their growth in the field was larger than the growth in industrialised countries, but the visibility of their research was limited. Universities were found to be the strongest producers of health biotechnology papers in the countries we studied. This study showed further that international research collaboration of these countries was extensive and domestic knowledge flows between their institutions seems to be increasing. Contrary to other work on health research in developing countries, this study suggested that developing countries' research was focused on local health needs.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.040 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".