Citation Analysis Shows Promise as an Effective Tool for Monograph Collection Development
Bibliographic record
Abstract
A Review of: Enger, K. B. (2009). Using citation analysis to develop core book collections in academic libraries. Library & Information Science Research, 31(2), 107-112. Objective – To test whether acquiring books written by authors of highly cited journal articles is an effective method for building a collection in the social sciences. Design – Comparison Study. Setting – Academic library at a public university in the US. Subjects – A total of 1,359 book titles, selected by traditional means (n=1,267) or based on citation analysis (n=92). Methods – The researchers identified highly-ranked authors, defined as the most frequently cited authors publishing in journals with an impact factor greater than one, with no more than six journals in any category, using 1999 ISI data. They included authors in the categories Business, Anthropology, Criminology & Penology, Education & Education Research, Political Science, Psychology, Sociology/Anthropology, and General Social Sciences. The Books in Print bibliographic tool was searched to identify monographs published by these authors, and any titles not already owned were purchased. All books in the study were available to patrons by Fall 2005. The researchers collected circulation data in Spring 2007, and used it to compare titles acquired by this method with titles selected by traditional means. Main Results – Overall, books selected by traditional methods circulated more than those selected by citation analysis, with differences significant at the .001 level. However, at the subject category level, there was no significant difference at the .05 level. Most books selected by the test method circulated one to two times. Conclusion – Citation analysis can be an effective method for building a relevant book collection, and may be especially effective for identifying works relevant to a discipline beyond local context.
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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 | 0.165 | 0.412 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.120 | 0.113 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
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".