A Retrospective Analysis of Leadership, Awardees, and Member Gender Representation of the Canadian Society for Immunology
Bibliographic record
Abstract
The Canadian Society for Immunology (CSI) established a formal Equity, Diversity, and Inclusion (EDI) Committee with the goal of providing EDI advocacy and leadership within the CSI, as well as in the broader scientific community. A first task of this committee was to review the publicly available historical data on gender representation within the CSI's membership, leadership, award recipients, and conference chairs/presenters as a step in establishing a baseline reference point and monitoring the trajectory of future success in achieving true inclusion. We found that, except for overall membership and a specific subset of awards, all categories showed a historical bias toward men, particularly prior to 2010. Bias persists in various categories, evident even in recent years. However, we note an encouraging trend toward greater gender parity, particularly in the roles of President, symposium presenters, and workshop chairs, especially from 2017 onward. We present these findings as well as our recommendations to enhance inclusivity. These include a more comprehensive collection and secure storage of self-identification data, emphasis on EDI as an essential component of all annual meeting activities, and innovative measures of outreach, collaboration, and leadership with the aim of making the CSI a model for improving EDI in other professional research societies.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".