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Enregistrement W6939818562 · doi:10.6084/m9.figshare.14110232

Responding to community feedback for supporting diversity in HPC & eResearch

2021· other· en· W6939818562 sur OpenAlexaboutno aff

Notice bibliographique

RevueFigshare · 2021
Typeother
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueMycorrhizal Fungi and Plant Interactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDiversity (politics)Variety (cybernetics)Digital divideGlobeTraining (meteorology)Community engagementInformation and Communications TechnologyProfessional development

Résumé

récupéré en direct d'OpenAlex

ABSTRACT / INTRODUCTION In November 2020, an Australasian Chapter of the global organisation Women in High Performance Computing (WHPC) was launched to better support diversity across the Australian and New Zealand HPC and eResearch sectors. As one of the first steps to connect with the community, the Chapter's founding organisations — NeSI, Australasian eResearch Organisations (AeRO), Monash University, NCI Australia, and Pawsey Supercomputing Centre — polled community members on what activities and initiatives they'd like the Chapter focus on in 2021. In this session, we will review the results of that community consultation, discuss how the top-ranked activities can be actioned, as well as dive deeper into what can be learned from past and other initiatives related to mentorship, recruiting & retention, professional development, and community-building for women in HPC and eResearch. ABOUT THE AUTHORJana Makar coordinates a variety of engagement initiatives and external communications to raise the profile of NeSI’s activities, impacts, and collaborations. Prior to joining NeSI, Jana spent more than a decade in communications roles with various organisations in Canada’s digital research infrastructure sector, from provincial research and education networks to regional and national high performance computing platforms. Megan Guidry is the Regional Coordinator for the Carpentries in New Zealand and also coordinates the training activities of New Zealand eScience Infrastructure (NeSI). Her main priority is raising eResearch capability in New Zealand through training delivery and community building. Lucy Guest's passion for STEM began on a sheep farm in Northern NSW where her childhood was spent exploring, experimenting and investigating. The National Youth Science Forum cemented ‘science’ as a career path, and a Bachelor Science/Law undertaken at UNE. It was the NYSF that brought her to Canberra, where she worked as the Marketing and Communications Officer, relishing the opportunity to introduce the joy of STEM to next generations. Lucy joined NCI as their Communications Manager in 2012 and is committed to championing women in HPC. Aidan Muirhead grew up in two Australian territories – the ACT and the NT – as well as Singapore and Serbia. She loved that maths gave her a universal language and has always wanted to know more about how things work. Passion for STEM and sharing stories led her to complete a Bachelor and Graduate Diploma in Science Communication at ANU. After 8 years at Questacon developing and delivering STEM programs across Australia, Aidan moved to NCI in 2019. Aidan is proud to support diversity in HPC, HPD, and eResearch. Kerri Wait's HPC journey began as an electronic engineering student simulating semiconductor devices during an industrial experience placement in Germany. Kerri has worked at a number of HPC and research computing facilities in Australia, collaborating with researchers to deliver scientific research that is faster, less painful, more robust, and repeatable. Kerri attended IBM’s EXITE program as a high school student, returning to speak as an early career professional, and is particularly interested in supporting women from low socioeconomic backgrounds to explore careers in STEM. Aditi Subramanya is a creative marketing and communications professional with more than 10 years’ experience in her chosen profession. She holds a Bachelor of Commerce specialising in Public Relations and Tourism and Event Management. She has been instrumental in providing global visibility in order to showcase Pawsey’s capabilities and services via key exhibitions at conferences worldwide, and plays a pivotal role in increasing market presence and overall brand awareness. Loretta Davis is a seasoned IT professional with 25+ years experience in the eResearch, commercial and government sectors in Australia, Africa and the USA. When not working part time for AeRO, Loretta consults as a Solutions Specialist to a number of private clients. Dr. Jenni Harrison is a passionate leader in technology and a positive role model. Jenni is an inclusive, strategic thinker who leads on national STEM initiatives, whilst mentoring others (presently a mentor for IMNIS and AIM WA). On 30th October 2020, Jenni was recognised by Women in Technology WA as a Tech [+] 20 Award Winner for 2020. Jenni is passionate about women in STEM and inclusion, is a Member of STEM Women, Women in STEMM, UN Women, WiTWA and is a Women in Data Science Ambassador for 2020. Jenni has presented on inclusion in STEM at several international conferences and events. An AICD graduate, with substantial governance experience, Jenni uses her skills to promote inclusion. In this regard Jenni is Chair of SHINE, a remarkable Not for Profit organisation based in the Geraldton region that collaborates with business and schools to actively engage with young female students who are at risk of disengaging from the conventional education system. Jenni is a lifelong learner and published author.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,037
score de la tête « metaresearch » (Gemma)0,081
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,067
Score d'incertitude au seuil0,224

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0370,081
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0180,006
Communication savante0,0120,010
Science ouverte0,0030,025
Intégrité de la recherche0,0080,010
Charge utile insuffisante (le modèle a refusé de juger)0,0670,012

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.

Tête enseignante Opus0,168
Tête enseignante GPT0,340
Écart entre enseignants0,173 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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