Does a Social Network Based Model of Journal Metrics Improve Ranking?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,009 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,008 | 0,017 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».