From the United States to China:The Transfer of Research Centres in Information Science
Bibliographic record
Abstract
This study not only analyses the centres of research in Information Science (IS), including the migration of central topics and central countries, but also analyses the relationship between the shifting of centres of research and their transformation. In addition, this study explores the relationship between the formation of the centre of research and the academic influence of the country on IS itself. We collected 25,150 articles, including 313,293 references about citation analysis, from databases SCI-E and SSCI between 1977 and 2016 as our data source. The following findings were obtained through this study: the transfer (transfer time) of central research topics in the IS domain has accelerated, from 12 to 8 years between 1980 and 1990, to 6 to 4 years between 2000 and 2010, and to 3 years between 2011 and 2016. The number of central research topics has also grown, from one between 1997 and 2006, to two from 2006 to 2013 to three from 2013 to 2016. The geographical centres of IS research were the US and Britain between the 1970s and 1980s, but gradually migrated through neighbouring countries, and finally to Asia by 2000. China, which became the centre of research for IS in 2005 for the first time, has been ranked first since 2011. In addition, countries acting as centres of research enjoy not only a high output of literature but also great academic influence. The theoretical and practical implications of our findings are discussed.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".