Bibliographic record
Abstract
An ambitious vision for design based on the premise that data is material, not abstract. Data analysis and visualization are crucial tools in today's society, and digital representations have steadily become the default. Yet, more and more often, we find that citizen scientists, environmental activists, and forensic amateurs are using analog methods to present evidence of pollution, climate change, and the spread of disinformation. In this illuminating book, Dietmar Offenhuber presents a model for these practices, a model to make data generation accountable: autographic design. Autographic refers to the notion that every event inscribes itself in countless ways. Think of a sundial, for example—a perfectly autographic device that displays information on itself. Inspired by such post-digital practices of visualization and evidence construction, Offenhuber describes an approach to visualization based on the premise that data is a material entity rather than an abstract representation. Emerson wrote, “Every act of the man inscribes itself in the memories of his fellows, and in his own manners and face.” In Autographic Design, Offenhuber introduces a model for design that emphasizes traces, imprints, and self-inscriptions, turning them into sensory displays. In an age where misinformation is harder and harder to identify, Autographic Design makes an urgent and persuasive case for a different approach that calls attention to the production of data and its connection to the material world.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.015 |
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".