Bibliographic record
Abstract
The central question of reverberating interferences – explorations into thingness, a recent performative research project, pertains to listening. Asking "How do things speak?" inspired investigation into what constitutes a thing, revealing potential imbalances arising from western metaphysics' denial of other matters' agency. Karen Barad's term of intra-action, which depicts a physicist-materialist interpretation of entanglement, is used to investigate this distortion theoretically and open up the question, “how can we listen?” Based on the conceptual framework of motion, the project's methodological focus was on the patterns of movement and countermovement created by human and animal bodies as intertwined indeterminacy (Barad) and the uncertainty of change (Rose). The aim of amplifying these ongoing interference reverberations before they become sedimentary and ingrained in planetary memory was to raise awareness that these interferences are manifestations of human and non-human planetary others, which include all mattering forms. Considering these 'noises' as an opaque, if not insignificant, voicing implies that what western modernity's metaphysics has declared non-belonging and irrelevant nevertheless matters in its warped and diverted forms. Movement-based intra-actions (Barad) are thus launched as audible noise, amplified via digital sensory devices as audible acoustic signals of resonant interferences. The project attempts to address a critical problematic in knowledge production intending to shift from a one one-world view (Law) towards a pluriversal approach. Emphasizing new materialism’s notion of situatedness establishes an opacity in which the researchers (performers) must confront their inability to classify ‘things’ outside of their educational epistemic realm. The flattening of de- and inhuMan categorizations (Wynter, 2015; Singh, 2018) necessitates a strategy to engage with other knowledges without appropriating them through 'our' inherited way of thinking. By considering critical positionality (Robinson), the notion of noise became an experimental indicator of the unknown or ignored. Noise increases the variability of information in information theory, with both extremes of too much or too little indicating a shift in the system’s exchange towards overload or flat sameness (Thompson, 2020; Malaspina, 2012). Inquiring as to how to facilitate a system's (ex)change with and towards the disregarded, suppressed, or simply denied, I wonder if allowing noise can enable a more balanced interaction with other matter and its knowings.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".