Planning virtual and hybrid events: steps to improve inclusion and accessibility
Bibliographic record
Abstract
Abstract. The past decade has seen a global transformation in how we communicate and connect with one another, making it easier to network and collaborate with colleagues worldwide. The COVID-19 pandemic led to a rapid and unplanned shift toward virtual platforms, resulting in several accessibility challenges that have excluded many people during virtual events. Virtual and hybrid conferences have the potential to present opportunities and collaborations to groups previously excluded from purely in-person conference formats. This can only be achieved through thoughtful and careful planning with inclusion and accessibility in mind, learning lessons from previous events’ successes and failures. Without effective planning, virtual and hybrid events will replicate many biases and exclusions inherent to in-person events. This article provides guidance on best practices for making online/virtual and hybrid events more accessible based on the combined experiences of diverse groups and individuals who have planned and run such events. Our suggestions focus on the accessibility considerations of three event planning stages: 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 events can help everyone engage more meaningfully, resulting in more impactful discussions that will more fully include contributions from the many 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.010 |
| 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".