Scene 1: Active Bystander and Inclusion Training Session
The conference room is bright, the sunlight spilling across the table. Patel, a few postdocs, and several colleagues are gathered for a formal active bystander and inclusion training session. Coffee cups sit on the table, and handouts are neatly stacked at each seat. The facilitator has invited female researchers to share experiences from conferences and departmental meetings, emphasizing how to intervene effectively as witnesses to bias.
A low murmur of nervous anticipation fills the room. Some participants glance at one another, unsure how much to share, while others shift in their chairs, fiddling with pens or coffee cups.
Patel begins, recounting a grant proposal feedback session where she was the only woman in the room. She had to defend that she even understood the basic ideas in the male candidates’ proposals — every concept, however straightforward, was questioned repeatedly, sometimes literally with "do you get it?". She imagines a tiny scoreboard ticking up every time her understanding is questioned. The males presenting their own proposals were met with nods and light clarifications, and were never asked if they understood. When it finally came to Patel’s turn, she presented her ideas, only to have her expertise subtly challenged at multiple points, forcing her to prove both her understanding and the validity of her own project.
Eva de Vries, the department chair, leans forward, asking, “As bystanders, did anyone notice these moments? Did anyone step in?” Patel shakes her head. The room murmurs, acknowledging the imbalance and the missed opportunities for intervention.
Joris, one of the male PhD candidates, then shares a conference story. A female researcher presents her work, and during her Q&A a male attendee indicates he doesn’t have a question for the presenter, but a comment. He then launches into a mini-monologue about the topic. Then he continues, “Actually, I do have a question, but not for the presenter — for *name male session host*, who probably has thoughts about this.” The woman interjects: “Actually, I have an opinion about this,” and proceeds to correct and clarify the discussion herself. Joris adds, dryly, “The room seemed to lean in, waiting to see how she’d handle it. I was so impressed by her poise.”
Chen shares her own experience from when she presented at another methods department. She remembers presenting a statistical model to a room full of faculty and PhD candidates, carefully explaining her approach. One male colleague immediately claimed that her model couldn’t be identified because of X. Chen calmly explained why the model was identified, repeating the explanation a second time when he persisted. Only after a male professor in the room reiterated her points did he finally back down. “He did apologize afterward for not believing me initially,” Chen adds.
Meera Patel adds, “It’s remarkable how consistent these patterns are across settings. And yet, having allies step in, even small gestures, can make a real difference.”
The facilitator leans forward and addresses the group: “We’ve focused on gender in these examples because we wanted to make sure many participants could share experiences, but the underlying issue is broader: race, (dis)ability, personality type, and other factors also shape who faces scrutiny or bias. Let’s continue exploring these dynamics.”
By the end of the session, several participants reflect aloud. One postdoc says, “I initially thought this training might not be necessary — I didn’t think there were serious problems here. But now I realize there are real patterns that create cumulative effects, and it isn’t about isolated incidents. It’s about the way these dynamics build up over time.” Others murmur agreement, acknowledging the insight.
Scene 2: Meeting Room, Late Morning
A few weeks after the training session, the meeting room is scattered with laptops and coffee cups. A visiting professor, a co-author on one of their ongoing projects, is walking the postdocs through a tricky problem on the whiteboard. Joris scribbles notes furiously and offers a couple of sharp observations along the way. The professor acknowledges these points with a slight nod and moves on, taking them in stride.
Patel leans back, arms crossed, listening. They’ve solved this derivation twice before breakfast. After a pause, Patel interjects, pointing out the missing step in two concise sentences. The logic clicks instantly.
The visiting professor blinks, genuinely surprised by the response. There’s a subtle shift in his posture, a tiny hesitation that wasn’t present earlier. “Wait — you got that immediately?”
Patel tilts their head, dry as ever. “Yes. I’m clever too.”
Across the table, Alex leans slightly forward, eyebrow raised — a quiet signal of recognition and support. Petrov, sitting nearby, adds clearly, “That is the brilliancy you should be expecting of Meera.”
The visiting professor clears his throat, mutters something about priors, still a little red-faced, and continues, a bit more cautious than before. The contrast is subtle, but the room senses a quiet tension.
Patel sips coffee slowly, exchanging a brief, knowing glance with Alex across the table, a small, mutual acknowledgment passing between them in the sunlight.
This story is based on input of female methodologists based in the Netherlands (with their consent), who shared their experiences with me. A small selection of those experiences was used as input for the AI prompts of the examples in this story, including the one in scene 2. The specific contexts have been altered to fit in the Alex Morgan setting. By exception, I will not share the exact prompts this time.