Trends of research productivity across author gender and research fields: A multidisciplinary and multi-country observational study
Notice bibliographique
Résumé
Bibliographic properties of more than 75 million scholarly articles, are examined and trends in overall research productivity are analysed as a function of research field (over the period of 1970-2020) and author gender (over the period of 2006-2020). Potential disruptive effects of the Covid-19 pandemic are also investigated. Over the last decade (2010-2020), the annual number of publications have invariably increased every year with the largest relative increase in a single year happening in 2019 (more than 6% relative growth). But this momentum was interrupted in 2020. Trends show that Environmental Sciences and Engineering Environmental have been the fastest growing research fields. The disruption in patterns of scholarly publication due to the Covid-19 pandemic was unevenly distributed across fields, with Computer Science, Engineering and Social Science enduring the most notable declines. The overall trends of male and female productivity indicate that, in terms of absolute number of publications, the gender gap does not seem to be closing in any country. The trends in absolute gap between male and female authors is either parallel (e.g., Canada, Australia, England, USA) or widening (e.g., majority of countries, particularly Middle Eastern countries). In terms of the ratio of female to male productivity, however, the gap is narrowing almost invariably, though at markedly different rates across countries. While some countries are nearing a ratio of .7 and are well on track for a 0.9 female to male productivity ratio, our estimates show that certain countries (particularly across the Middle East) will not reach such targets within the next 100 years. Without interventional policies, a significant gap will continue to exist in such countries. The decrease or increase in research productivity during the first year of the pandemic, in contrast to trends established before 2020, was generally parallel for male and female authors. There has been no substantial gender difference in the disruption due to the pandemic. However, opposite trends were found in a few cases. It was observed that, in some countries (e.g., The Netherlands, The United States and Germany), male productivity has been more negatively affected by the pandemic. Overall, female research productivity seems to have been more resilient to the disruptive effect of Covid-19 pandemic, although the momentum of female researchers has been negatively affected in a comparable manner to that of males.
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 | 0,005 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,011 | 0,023 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».