Bibliographic record
Abstract
A Review of: Bollen, J., Van de Sompel, H., Smith, J.A., & Luce, R. (2005). Toward alternative metrics of journal impact: A comparison of download and citation data. Information Processing and Management, 41:1419-1440. Abstract Objective – To test a new model for measuring journal impact by using principles of social networking. Research questions are as follows: 1. Can valid networks of journal relationships be derived from reader article download patterns registered in a digital library’s server logs? 2. Can social network metrics of journal impact validly be calculated from the structure of such networks? 3. If so, how do the resulting journal impact rankings relate to the ISI impact factor (IF)? Design – Bibliometric, social network centrality analysis Setting – Los Alamos National Laboratory (LANL), New Mexico Subjects – 40,847 full-text articles downloaded from a large digital library by 1,858 unique users over a 6 month period. Methods – Full-text article downloads from a large digital library for a six-month period were examined using social networking analysis methods. ISSNs for journals in which the retrieved articles were published were paired based upon the proximity of use by the same user, based on the supposition that proximal downloads are related in some way. Reader-Generated Networks (RGNs) were then tested for small-world characteristics. The resulting RGN data were then compared with Author-Generated Networks (AGNs) for the same journals indexed in the Institute of Scientific Information (ISI) annual impact factor (IF) rankings, in the Journal Citation Reports (JCR) database. Next, a sample of the AGN-derived pairings was examined by a team of 22 scientists, who were asked to rate the strength of relationships between journals on a five-point scale. Centrality ratings were calculated for the AGN and RGN sets of journals, as well as for the ISI IF. Main results – Closeness and centrality rankings for the ISI IF and the AGN metrics were low, but significant, suggesting that centrality metrics are an acceptable impact metric. Comparison between the RGN and ISI IF data found marked differences, with RGN mirroring local population needs to a much higher degree, and with a non-significant correlation between the ISI IF and RGN ranking, while AGN and RGN centrality rankings show significant centrality and closeness and betweenness correlations. RGN network ranking identified highly localized foci of interest for the LANL, as well as “interest-bridging” subject areas pointing to possible emerging interests among the scientists. Conclusion – The study results appear to successfully demonstrate an alternative to existing journal impact ranking that can more validly and accurately reflect the practices of a local community. The authors suggest that the social network-derived methodology for identification of impact rankings avoids biases intrinsic to ISI IF as a result of frequentist metrics collected from a global user group. Although the authors resist the idea of generalizability due to the local nature of their data, they suggest that the methodology can be successfully used in other settings, and for a more global community. Finally, the authors propose the automated creation of an open-source RGN whose data could be localized for smaller communities, with potentially large implications for the existing publishing industry.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".