Structural analyzing of “Information Science Theories’ based on co-word network analysis of articles in Web of Science database (1983-2017)
Bibliographic record
Abstract
The formation of intellectual currents, methods of analysis in science and methodology are the most interesting topics of science and philosophy of science. Theories of science are made up of facts that have been gathered together, arranged and discussed. This study aims to analyze the articles regarding information theories based on the concepts of co-occurrence network analysis and centrality indicators published in Web of Science during 1983-2017. This is a descriptive study, using scientometric techniques. Its statistical population contains all clinical trials related to information theories in Web of Science during 1983-2017. The scientific research on theories of information existed from 2009 to 2014. Based on the scientific map of countries, theories of information have been active in some countries including USA, Germany, United Kingdom, Spain and Canada and Brazil and China have also been active in research on this filed in these years. The top authors in theories of information field in Web of Science during 1989-2017 are Kimball, Bruner, Hersink, Lochs, and Nendersen. The mentioned authors are considered the most scientific co-authorship. Likewise, Hjørland, Hartle, White, Bushman, and Bruner have the most abundant published articles. Also, the co-authorship ratio of these authors is 0.3, which indicates a relatively high level of corporate. The analysis of information theories also showed that based on co-word analysis there are 9 clusters. Among them cluster 1 is the largest and is related to algorithms and models in theories. Other clusters are information theories-related entropy theory and its application, information theorems derived from other sciences, thermodynamics theory and physics in information science, the theory of communication and communication models, the use of interdisciplinary information theories, the effectiveness of theories, the evaluation of theories, the role of information and the role of host and importer of theories in information science and science.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.010 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".