Planning virtual and hybrid events: steps to improve inclusion and accessibility
Bibliographic record
Abstract
Over the last decade, the way we communicate and engage with one another has changed on a global scale. It is now easier than ever to network and collaborate with colleagues worldwide. But, the COVID-19 pandemic led to a rapid and unplanned move to virtual platforms, resulting in several accessibility challenges and the inadvertent exclusion of several people during online events. While virtual/hybrid events have strong potential to facilitate new opportunities and networks for everyone, they are also greatly positioned to increase the inclusion of groups traditionally excluded from purely in-person conferences. However, early and careful planning is needed to achieve this, with inclusion and accessibility considered from the start. Including a virtual element in a conference does not automatically equal inclusion or accessibility. Without effective planning, virtual and hybrid events will replicate many biases and exclusions inherent to in-person events.This presentation will share lessons learned from previous events’ successes and failures, based on the combined experiences of several groups and individuals who have planned and run such events. This presentation is based on an EGU Sphere article, of the same title, that aims to provide guidance on planning online/hybrid events from an accessibility viewpoint based on the authors experiences. The goal of this presentation is to initiate discussion on event accessibility and inclusion and to help generate new ideas and knowledge from people outside of the authors network. Every event is unique and will require its own accessibility design, but early consideration is crucial to ensure everyone feels welcome and included. Our suggested accessibility considerations have been broken down into three stages of event planning: 1) Pre-event planning, 2) on the day/during the event, and 3) after the event.Ensuring accessibility and inclusivity in designing and running virtual/hybrid events can help everyone engage more meaningfully, resulting in more impactful discussions including groups with limited access to in-person events. However, while this article is intended to act as a starting place for inclusion and accessibility in online and hybrid event planning, it is not a fully comprehensive guide. As more events are run, it is expected that new insights and experiences will be gained, helping to continually update standards.
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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 | 0.029 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".