Scholarly Publishing in Mozambique: Research Institutions, Researchers, and Articles
Bibliographic record
Abstract
This article describes scientific journals indexed in the Web of Science from 2000 to 2015 featuring Mozambican authors or researchers. The study sample included 1,536 articles in which 896 Mozambican authors and their institutions were identified. Mozambican authors published primarily in subscription-based journals from the United States and the United Kingdom. A Google Scholar search for the same authors yielded 423 documents, including articles published in 72 journals in 14 countries. Mozambican research is often published in English with an international partner in Western countries.Cet article décrit des revues savantes indexées dans le Web of Science entre 2000 et 2015 qui incluent des auteurs ou chercheurs mozambicains. L’échantillon de cette étude comprend 1 536 articles dans lesquels on a identifié 896 auteurs mozambicains et leurs institutions. Les auteurs mozambicains ont principalement publié dans des revues par abonnement américaines et britanniques. Une recherche de ces mêmes auteurs avec Google Scholar a relevé 423 documents, y compris des articles publiés dans 72 revues de 14 pays. La recherche mozambicaine est souvent publiée en anglais en collaboration avec un partenaire d’un pays occidental.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.019 | 0.060 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".