Análisis de dominio sobre riesgos y clima en la Web of Science
Bibliographic record
Abstract
Design/Methodology/Approach: The study has a justified quantitative approach in the bibliometric methods and the social networks analysis. The Web of Science database allowed to recover the scientific production on Risks and Climate. Primary indicators are calculated, and multivariate representations of the domain are made. \nResults/Discussion: Increases in scientific output were identified in 2006 and 2008, where the variation rate shows its highest expression. There is high productivity and collaboration in the United States, England and Australia respectively, and the participation of Latin American countries in the scientific production of the subject was identified. James D. Ford and Tristan Pearce are the authors with the largest number of collaborative works (13 articles) on the topics of climate change in the Canadian Arctic and adaptation of the Eskimos. Environmental Sciences and Ecology (Environmental Science & Ecology) predominate in thematic categories. The most influential journals have an impact factor greater than 4. The most cited author is the Intergovernmental Panel on Climate Change (IPCC), the highly cited journals were: Climatic Change and Global Environmental Change-Human and Policy Dimensions. \nConclusions: Domain analysis reveals patterns that cannot be observed with the naked eye in the thinking and language of professional groups. Bibliometrics is the most widespread and used approach. The study allowed us to carry out an in depth analysis of the topic Risk and Climate, identifying the features that characterize it in the scientific production indexed in the Web of Science. \nOriginality/Value: It is a topic that worries the scientific community worldwide, based on the growing increase in articles on the subject. The study is a reference for future research on risks and climate. It meets a demand from the Coastal Ecosystem Research Center of Ciego de Ávila in Cuba, which needed to know the scientific production on this topic for its research and scientific development.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.048 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 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".