Active demographic data collection in geoscience: results, implications, and recommendations from a survey of Canadian academia  
Notice bibliographique
Résumé
Many universities openly pledge commitments to improving diversity, with science, technology, engineering, and math (STEM) fields receiving significant attention. Despite these efforts, geoscience remains one of the least diverse fields in STEM. This recognition has prompted an increase in studies stressing the systemic lack of representation across the field and the barriers that exist for those within. However, much of this work has been limited by the use of demographic datasets that have been either passively collected or derived from government sources. Constraints include country-specific data collection policies, failures to collect field-specific data, and the absence of additional information necessary for intersectional analysis. Advancing diversity, equity, and inclusion (DEI) in our field requires meaningful datasets that clearly identify social inequalities. Limited, incomplete, or anecdotal data are too easily dismissed by those in power, stalling constructive efforts.In Canada, demographic data is not regularly collected at academic institutions and is seldom field-specific. This absence of data undermines efforts to identify the current state of diversity in the field and prioritise initiatives for improvement. Collecting comprehensive demographic data is a crucial step in determining whether progress is evident. It can also help to highlight areas of concern, especially in fields lacking in diversity, such as geoscience. To address this absence of data, we disseminated a 22-question demographic survey to 35 academic geoscience departments across Canada in late 2022.We received 482 eligible responses to the survey, accounting for approximately 20% of the research population. Overall, men make up a slight majority across all respondents (53%), and the percentage of individuals who identify as white (73%) is greater than the national average (67%). Additionally, results shows that research students (MSc and PhD) are a diverse group, while salaried positions (postdoc, research staff and faculty) lack diversity in a wide range of categories including, gender, race, LGBTQ+, Indigeneity, and disability. Moreover, tenured positions are overwhelmingly occupied by white men, with racial inequalities prominent in the data.These data highlight several areas of concern in the academic career path. The transition from research student to salaried research remains a clear area of concern, while the tenure process appears to continually favour white able-bodied cisgender men. Moreover, the representation of Indigenous persons and those with self-identified disabilities remains very low. Solutions require institutional changes to recruitment, tenure applications, postdoctoral hiring, field work design, and mentoring practices. Importantly, they also require changes to how we collect and analyse demographic datasets in geoscience, as a continued reliance on data that is passively collected or obtained from government sources will continue to limit our abilities to identify areas of concern and create effective strategies.
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 | 0,038 | 0,127 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,013 |
| Études des sciences et des technologies | 0,008 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».