Bibliographic record
Abstract
Compression is an essential technique used across diverse information systems, one in which supposedly redundant or superfluous information is minimized or eliminated in order to make the storage, transmission, or reception of other information more legible or efficient. Compression is involved in everything from computer data storage (encoding) and efficient computational processes (floating point arithmetic) to the formatting of media (telephony, radio, streaming) or the engineering and circulation of sound and image (dynamic compression of volume, jpeg resolution). Beyond any particular technical implementation, though, compression names the peculiar perceptual regime of late modernity—it is our percepteme, our episteme. As Galloway and LaRivière (2017) have noted—everything is compressed, from the logic of digital computers to our attention spans. Yet, if compression designates the essence of experience today, it is precisely in response to a complementary concept of noise. Noise is the lived affect of our material conditions which cannot be made significant to us: not only the literal acoustic noise of late modernity (the waste-product of technologies which hang over perceptual spaces like smog hangs over cities) but, perhaps more critically, compression emerges to cope with a new, properly “cognitive complexity” embodied in the unprecedented entanglement and mediation of social relations through the technical/computational unfolding of the value-form of capital. Such complexity—lying beyond the grasp of any human individual—is logistically offloaded onto the nootechnical externalizations associated with, for instance, AI algorithms. On the other hand, it is individuated in experience as noise. Because such noise is perceptually intractable, compression becomes the necessary shape of our aesthesis, one that rigorously flattens the available modalities of experience/value—yet giving birth to new forms of abstraction, perception, and thought. While several thinkers—notably, Jason LaRivière and Cécile Malaspina—have brilliantly elaborated each concept on its own, I argue that they can only be properly situated through each other. In order to do this, I combine computational and information theory, philosophy, media and cultural theory, as well as political economy with concrete domains where compression and noise articulate the phenomenological stakes for aesthetics today.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".