Definition and Measurement of Core Collaborators in Scientific Research Collaboration: A Measurement Methodology Based on the H-Index
Notice bibliographique
Résumé
[Purpose/Significance] Different collaborators play different roles and assume corresponding responsibilities in scientific research collaboration. Distinguishing the different roles in research collaborators is important for the evaluation of research talent and human resources allocation. Previous studies have defined the roles of collaborators from multiple perspectives, both qualitative and quantitative, but lack a simple and efficient way to identify core collaborators. In this paper, we use the number of collaborations to identify core collaborators in scientists' collaborative relationships based on the H-index measure, which is a very easy to calculate and intuitively understandable method. [Method/Process] Using the OpenAlex database as a data source, an empirical analysis of approximately 5.05 million journal papers in the field of computing in China and G7 countries over 20 years (2000-2021) was conducted. First, the core collaborators of highly productive scientists were studied and their collaboration characteristics were analyzed from the perspective of size and share. Second, based on the H-index fitting formula proposed by previous authors, a formula for estimating the number of core collaborators based on the number of publications and the average number of collaborators per article was proposed. Finally, the formula was used to compare the differences between the theoretical and actual values of the number of core collaborators across countries. [Results/Conclusions] The study found that in terms of size and proportion of core collaborators, China had the highest average total number of collaborators among highly productive scientists, followed by the USA, Germany and the UK, while Italy had the lowest. The number of core collaborators was generally 3-7 across countries, with China and Italy having a higher rate of cooperation and the UK, France and Canada having a lower rate of cooperation. In terms of the number of core collaborators as a percentage, no country has more than 10%, with Italy having the highest percentage of core collaborators at 7.42%, followed by Japan, France and Canada, while the US has the lowest percentage of core collaborators. In terms of the total number of collaborators, there is no significant difference between China, the US and Germany, while there is a significant difference among all five other countries. In terms of the number of core collaborators, China is not significantly different from Italy and is significantly different from all other six countries. The number of core collaborators can be estimated by using the formula of the product of the number of publications and the power of the average number of collaborators per article, which has a good fit of 0.8 or more. Among China and the G7, the US, Germany and the UK have a lower proportion of core collaborators, with more frequent mobility and exchange of talent, while Italy, Japan and China have a higher proportion of core collaborators, indicating a lack of talent mobility and a relative consolidation of research collaboration.
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 | 0,020 | 0,080 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,010 | 0,014 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,007 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».