"What They Say is What They Mean": Listening to Someone's Story
Notice bibliographique
Résumé
"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...
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».