The Women of FOCIS: Promoting Equality and Inclusiveness in a Professional Federation of Clinical Immunology Societies
Bibliographic record
Abstract
The authors of this article, all women who have been deeply committed to the Federation of Clinical Immunology Societies (FOCIS), performed a retrospective analysis of gender equality practices of FOCIS to identify areas for improvement and make recommendations accordingly. Gender data were obtained and analyzed for the period from January 2010 to July 2021. Outcome measures included numbers of men and women across the following categories: membership enrollment, meeting and course faculty and attendees, committee and leadership composition. FOCIS' past and present leaders, steering committee members, FCE directors, individual members, as well as education, annual meeting scientific program and FCE committee members and management staff of FOCIS were surveyed by email questionnaire for feedback on FOCIS policies and practice with respect to gender equality and inclusion. Although women represent 50% of the membership, they have been underrepresented in all leadership, educational, and committee roles within the FOCIS organization. Surveying FOCIS leadership and membership revealed a growing recognition of disparities in female leadership across all FOCIS missions, leading to significant improvement in multiple areas since 2016. We highlight these changes and propose a number of recommendations that can be used by FOCIS to improve gender equality.
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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 | 0.037 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".