Bibliographic record
Abstract
Reverb-Resonate: Sounding the Affective Frequencies of Migration operates at the intersection of art, science, and technology to articulate an emotional landscape of migration and exile. Rooted in the methodology of research-creation (RC) and grounded in the interdisciplinary field of Art, Science, and Technology Studies (ASTS), the project transcends conventional disciplinary boundaries to offer speculative possibilities through human-machine listening. Drawing on the body is an already augmented site, the project makes audible physiological sensors that capture micro-level intricacies responsible for stress regulation. Listening, is, thus, foregrounded as the core public engagement strategy, creating a layered sound collage that interweaves somatic registers of recorded breathing samples and physiological sensor values with machine listening to recreate new forms of sound. Engagement with ASTS is, hence, in the form of a method that traverses transforming sensor application, generating technicized sound and composing an acoustic experience capable of affective engagement. Through machine learning—a subfield of artificial intelligence—the notion of ‘machine listening’ extends beyond human hearing limitations, introducing non-normative structures to challenge and expand habitual forms of human listening. Reverb-Resonate , hence, leverages artistic strategies and techno-augmentations to address the crisis of imagination that hinders opening up to realities far from the familiar and the personal to imagine ‘what could be’ as a way of future-making. It underscores the critical edge of RC and ASTS in addressing complex critical issues, proposing a speculative space where the human-machine hybrid puts forth a socio-technical assemblage of listening to understand 'otherly' experiences. The project, thus, advances a critical inquiry into the mediation and augmentation of listening to imagine new possibilities for embodied engagement with unfamiliar emotional spaces and experiences.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".