So, you want to host an inclusive and accessible conference?
Bibliographic record
Abstract
[p.1]: "In 2017 at the Gender Summit in Montreal, Québec, Chief Science Advisor of Canada, Dr. Mona Nemer, stated the importance of promoting diversity in Science, Technology, Engineering, and Mathematics (STEM). She emphasized that “increasing the number and impact of women and other members of underrepresented groups in STEM requires the concerted efforts of our entire society—including governments, scientific organizations, research granting agencies, and educational institutions” (Nemer 2017). The following year, the Tri-Council (Natural Sciences and Engineering Research Council, Canadian Institutes of Health Research, and Social Sciences and Humanities Research Council) created an Equity, Diversity and Inclusion (EDI) Action Plan, with one of its key objectives being the development of initiatives to foster inclusive participation in the research system (Canada Research Coordinating Committee 2018). The aim of these initiatives is to increase participation of the four designated groups—women, persons with disabilities, members of visible minorities or racialized groups, and Indigenous Peoples—and members of the LGBTQ2+ communities as mandated by the Tri-Council (Government of Canada 2019)."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.227 | 0.084 |
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".