From the United States to China:The Transfer of Research Centres in Information Science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it