Building a Women in HPC community in Australasia: Where to from here
Bibliographic record
Abstract
Diverse teams have been shown to increase a company’s ROI, make decisions 2X faster with half the meetings, and corporations that hire women into the C-suite often see a 15% increase in profitability. In this eResearch NZ Birds-of-a-Feather (BoF) session, we would like to build on the momentum and conversations started at a previous eResearch Australasian BoF and discuss the opportunities around better coordinating, supporting, and expanding diversity efforts within Australasia’s eResearch and HPC sectors. This BoF’s ideal outcomes would include: ● a greater understanding of existing efforts and support structures in place for encouraging diversity and gender equity in Australasia’s HPC and eResearch sectors ● building new and/or stronger relationships built amongst Australasia diversity and gender equity advocates ● a short-term action plan for 2020-21 to explore ways to better connect, coordinate, and leverage existing diversity and inclusion efforts Discussion notes and feedback gathered in this session will be collated and shared with the broader eResearch community, as part of a newly formed working group’s efforts to support the development of a Women in HPC (WHPC) community in Australasia. For more information on the Australasian WHPC Working Group, visit the AeRO eResearch Chat’s Diversity & Inclusion space. ABOUT THE AUTHOR(S) Jana Makar Based at the University of Auckland, Jana 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 worked as a communications consultant for multiple organisations in Canada’s technology, academic, and startup sectors. Loretta Davis Loretta 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".