Bibliographic record
Abstract
Listening has always been undervalued as a mode of participation both inside and outside Media Studies (Fiumara 1995, Lacey 2013, Lipari 2014). Applying definitions of listening from philosophy, feminist political theory, media studies, and documentary studies to software studies, this paper identifies problematic practices and provides redefinitions and reframes: Listening only qualifies as listening when the listening itself has an effect or changes the listener, in the moment or later. As examples of a device of meaningful listening that might be helpful in software design, the very medium used for interactive media, the paper examines two documentary case studies: Lana Lin’s The Cancer Journals Revisited (2018) and Irene Lusztig’s Yours In Sisterhood (2018). Both filmmakers invited volunteers to read aloud on camera; in the case of Lin, the twenty-seven writers, artists, activists, health care advocates, and current and former patients she invited read Black lesbian feminist poet Audre Lorde’s classic 1980 memoir of her breast cancer experience. And in the case of Lusztig, she recruited participants in 32 different US states to read aloud and respond to a selection of letters to the editors at Ms. Magazine from the 70s covering issues of divorce, abortion, rape, discrimination, among many other feminist issues of the day. Both filmmakers also made it clear that they had invited these readings and exposed in multiple ways the mechanics of these readings. The paper argues that both reading aloud and the exposition of the request to read, as a device in the documentaries, is a form of listening and analyzes this through a framework of listening as a participatory process. The paper also identifies the paradiscourses of both projects and the relationships that the respective filmmakers hope to develop with their readers and viewers. Both projects embody a kind of participatory listening that suggests a much-needed antidote to the overlooked practice in software design that assumes listening cannot be participatory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".