Bibliographic record
Abstract
With today’s increasingly digitized culture, we are witnessing an ideological shift toward paperless communication and the emergence of the digital page. Yet, we continue to conceptualize the visual structure of information using the language of print, imposing unnecessary limitations. Recent efforts in e-book development most vividly highlight the need for study of the distinct features of the electronic format and, in turn, the associated range of effects on the way we interact with information. In the first half of the present paper, I situate the notion of the page in multiple socio-historic and theoretical contexts, rationalizing its broad viability as a visual solution for the digital display environment. In the second half, I describe some of the characteristics of digital pages, as viewed with a conventional personal computer, using examples from a cross-section of functional contexts, including Adobe Reader, NYTimes.com, Twitter, YouTube, and Google Maps. Drawing on the field of information design, I apply visual analysis to general characteristics (an exploratory term comprising dimensions, blank space, colour, content, printability, and interactivity), composition, and typographic legibility. Based on a very limited data set, my findings indicate that digital pages currently have a distinctly vertical orientation, requiring extensive use of scrolling, and do not utilize the full area of the computer screen. They offer a dynamic multimedia experience that does not lend itself to printing. Simple, streamlined grid structures and proven proportional relationships are found to produce the most balanced and accessible compositions, while typographic legibility is found to suffer from excessive column width. I thus generate an introductory sketch of the basic structure of the digital page to help advance our understanding of the electronic interface
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.182 | 0.074 |
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".