Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it