"What They Say is What They Mean": Listening to Someone's Story
Bibliographic record
Abstract
"What They Say is What They Mean":Listening to Someone's Story Nina Sun Eidsheim (bio) and Juliette Bellocq (bio) In western academia and colloquially, listening to music is often about measuring. What I mean by that is that listening is used to assess. Is the sound too loud, too quiet, or just so? Is someone out of line (singing out of tune, or too loudly)? Are they right or wrong (did they play the tune correctly)? There are, of course, all kinds of problems associated with this type of listening, and I have spent the last twenty years addressing this issue. To name some of the problems: first, this kind of listening assumes there is an essential stable object to identify; for example, a knowable, unaltered, "in-tune" pitch. However, any so-called identifiable sound is conjured from a musical-cultural context and a value system. Take something seemingly objective, like singing in or out of tune. What might be considered out of tune within one scalar context can be in tune in another. Second, while there are many different people and a variety of listening and value systems within any given society, if only one such system is deemed correct, all but that one will be repressed by the few in power. I have dedicated my career to illuminating the limits of this kind of one-dimensional listening and its ramifications. As listening is a total system—meaning it is defined, legislated, promoted, and disputed across the lexical, conceptual, analytical, and sensorial domains—I created the [End Page 307] Practice-based Experimental Epistemology Research (peer) Lab at ucla to engage more listening strategies and consider other value systems. Part of what I wished for the peer Lab was to communicate our findings in more ways than just academically organized and presented arguments. I invited graphic designer Juliette Bellocq to work with me on this. Together, we work to transfer or metabolize an idea from one domain to another—say from words and logic to visuals and brief, non-argument-driven writing. We also work to translate ideas that seem to live in so-called sonic worlds (for example, sound) to visual worlds. While all this work has been so inspiring and has added so much to the Lab, the most radical move for me has involved stepping back and learning more about Juliette's relationship to and approach to listening. In contrast to the way I have been encultured to listen, and even to the interventions I have made in that regard, I have learned from Juliette that graphic designers use listening 1) to learn new things; 2) to really hear what people are saying, instead of assuming that it needs translation; and 3) to continuously calibrate and make sure they're hearing the stories being told (as opposed to, for example, inventing subtext). The following is an excerpt of a conversation between Juliette and myself on this topic where I learn about how she uses listening as a tool in her work, and how she thinks about how she needs to listen in order to do so. Nina: What is listening for a graphic designer? Juliette: As a graphic designer, I agree to not be the single author of the content of my work. Graphic design, in my practice, means sharing content. I place myself in a situation where I get to translate something I've heard, understood, or seen or reconfigured, and so that means that I have a voice—I am an author—but there is a co-author as well. It can be a client or a community. So listening is essential. As you know, besides working with the peer Lab, I mainly work with architects in the making of spaces. And the key question when we visit a space or when we meet with people is, what are their stories? Listening is our primary tool and resource. Nina: Do you listen similarly or differently from architects, or even from graphic designers? If so, how do these kinds of listenings come together? Juliette: I do think that I listen differently than some other designers because my primary goal is...
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".