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Transcript

EP 1: Managing HEA/HFN Expectations

Hear Us Out Romance Podcast Series

Hear Us Out… is a podcast series where Katrina Jackson and I bring our offline romance conversations into the public space. As longtime readers of the genre, we talk through what romance is doing, how it works, and why it matters. Each episode is an invitation to continue the conversation well after the mic is off.

In this episode, we discussed how the HEA/HFN in romance and reader expectations might not always match.


Remember the bit of a spiral or epiphany I had? You know, the idea that romance isn’t the same thing as love and that an HEA or HFN might simply mean the characters end up together, not necessarily that everything is resolved the way we expect it to be. That realization opened up a larger conversation about how subjective romance really is. What we each want from a story can look wildly different. Are you reading for character growth? Low angst? or for specific tropes or ones you avoid at all costs?

Even our expectations for what an HEA or HFN could look like aren’t universal. I’m drawn to character work. Katrina points out that second-chance stories can be a place where that happens, and the ending is plausible for romantic love to exist. In further discussion, we zoomed out to acknowledge that romance hasn’t always looked the way it does now…

romance has not always looked the way that it looks today. Even the way that it looks today is shaped by your particular algorithm, what you like to read, what you share with people.

I’m left to wrestle with the possibility that my expectations may need a reset.

So if you’re interested in hearing more, check it out, and let us know where you land.

Reflection prompts:

  1. How do your expectations for an HEA or HFN shape your satisfaction with a story?

  2. What are you most often looking for when you read romance right now (e.g., character growth, emotional safety, high or low angst, specific tropes)?

  3. How have those preferences changed over time or been influenced by the algorithm?

Happy Listening!

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