Dematerializing Digital Objects: Denial, Decay, Detritus and Other Matters of Fact
Bibliographic record
Abstract
We know little about the materials that constitute the digital devices we use every day, from where those materials are derived, or where they will go when we discard them. Through a variety of means, digital devices are “dematerialized.” That is, a digital object’s material components are denied and concealed by complex cultural and economic practices that support a myth of immaterial and ubiquitous computing without material consequences. Since the early days of digital computing, designers have striven to design devices that are smaller, better, denser, and faster. These traits are framed as ideals against which new products are measured and they have encouraged a desire for ubiquitous, imperceptible integration of digital computing at all levels of modern life. This dissertation argues that the digital object is dematerialized and that this pervasive reduction of the physical object and our very awareness of the physicality of digital materials inhibits our ability to support awareness of the material limits and often detrimental impacts of digital devices. However, the material nature of the digital object may be more apparent after an object is rendered obsolete. Drawing from media archaeology, thing theory, and material culture studies, this dissertation examines a few “afterlives” of digital objects because it is only after its useful life that the object’s materiality takes on transformative powers. For example, when discarded, its physical properties become problematic and may be framed as an environmental issue. Or, when treated as a material artifact in a museum the digital object resists historicity, and when saved as a memento it may take on unexpected nostalgic power. I argue that it is precisely the dematerialized aspects of the informatic media that have created the situation of ‘e-waste’ and it is through a new consciousness of their materiality that we might think about how these technologies evolve and occupy space in the future.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.132 |
| Scholarly communication | 0.026 | 0.050 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".