Equity, Diversity and Inclusion in Canadian immunology: communication and complexity
Bibliographic record
Abstract
The Canadian Society for Immunology (CSI) organized an Equity, Diversity and Inclusion (EDI) training workshop during its 2022 Scientific Meeting to improve understanding of EDI and explore strategies to achieve EDI goals in the scientific environment. The workshop focused on identifying Specific, Measurable, Achievable, Realistic and Timely (SMART) goals related to EDI in academia through small group discussions and learning exercises. Attendees highlighted several equity considerations within the field of academic immunology, including financial barriers, lack of diversity in research teams and gender bias; they emphasized the importance of creating an inclusive and accessible research environment. The collection and use of data relevant to EDI goals within the CSI were also identified as challenges. Fostering a culture of active and nonjudgmental listening within the CSI community is another aspirational goal to address EDI. The workshop received positive feedback from attendees, who noted that more diverse voices and specific actions for local research environments are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.035 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".