Moving Towards More Inclusive Conferences and Events: Reflections on Establishing a Diversity, Equity, and Inclusion Chair
Bibliographic record
Abstract
As a part of the continued movement towards Diversity, Equity, and Inclusion (DEI), the IEEE Professional Communication Society (ProComm) has recently established a DEI Chair for the ProComm 2023 conference. In an effort to document and promote discussion on initiatives towards more inclusive conferences, this brief paper presents a case study about the establishment of that chair and the work done to continuously improve diversity and inclusivity at the conference and beyond, in particular, addressing the needs of the smaller conference within the context of a large organization like IEEE and how to make the DEI position both manageable and effective within the constraints of a smaller society. The goal of this paper, therefore, is to explore the current and future plans for this chair at the ProComm Conference, with the recognition that while there may be no such thing as a perfectly inclusive conference, we can certainly continuously improve in that direction.
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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 | 0.090 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.018 |
| Scholarly communication | 0.032 | 0.018 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.011 | 0.039 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".