UK Library and Information Science Research is Having a Significant Influence on Research in Other Subject Disciplines
Bibliographic record
Abstract
Objective – To quantify the value of librarianship and information science (LIS) exports knowledge to other subject disciplines. Design – Bibliometric study. Setting – LIS departments in U.K. universities. Subjects – 232 LIS research articles published between 2001 and 2007. Methods – Data from the 2008 U.K. Research Assessment Exercise were checked to identify 405 research articles submitted by 10 selected university departments (out of a total of 21), which submitted research in the LIS category. The Web of Science database was then searched to see how many of these articles had been cited in other articles (n=232). If the citing article was published in a non-LIS journal it was considered a knowledge export. Journals were defined as non-LIS if they had not been assigned the subject category of Information Science & Library Science by the Journal of Citation Reports. The journal Impact Factors (IFs) of citing journals were then normalized to measure the value of individual knowledge exports to their respective subject disciplines. This was done by comparing a citing journal’s IF with the median journal IF within that subject category. If the citing journal’s IF was above this median it was considered to be a valuable knowledge export. Main Results – The sample of LIS research articles produced a total of 1,061 knowledge exports in 444 unique non-LIS journals. These non-LIS journals covered 146 unique subject categories of which those related to computer science and chemistry/pharmacology cited LIS research with the greatest frequency. Just over three-quarters (n=798) of these citations were considered to be valuable knowledge exports. A sub-analysis showed that LIS articles published in non-LIS journals were significantly more valuable than the knowledge exports published in LIS journals. Conclusion – The validity of bibliometric studies can be improved by adopting the two methodological innovations presented in this study. The first innovation is to avoid over-estimating the number of knowledge exports by discounting “part exports” (i.e., where the citing journal is assigned to multiple subject categories, one of which includes the same as that of the cited reference). The second innovation introduced by this study is to add an extra dimension to the analysis by measuring the value of each knowledge export by taking into account the “normalized” impact factor of citing journals.
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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 | 0.016 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.055 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".