After the editing is done: Designing a Graphic User Interface for digital editions
Bibliographic record
Abstract
With its origins dating back only to the second half of the twentieth century, Computer Science can be considered a very young discipline. The widespread adoption of the "Personal Computer" is even more recent. Introduced in the 1970s, its ascendancy was recognised by Time Magazine as recently as 1983 (Time Magazine 1983). As a logical consequence, the study of User Interface design for computing devices is even younger. Indeed the idea of a Graphical User Interface (GUI) is quite a novelty: it is possible to trace a clear evolution from a non-graphical interaction by means of physical devices (punchcards and readers) to the CLI (Command Line Interface), where you type instructions that the computer will execute, and finally to the recent advent of the GUI and its WIMP (Windows, Icons, Menus and Pointing device) paradigm. So recent indeed is this development that a number of crucial key concepts and theories that govern their use were conceived a long time before an actual implementation would become feasible. The word "hypertext," for example, was coined by Theodor Nelson, who later worked at a Hypertext Editing System at Brown University, in the mid sixties (see Nelson 1965).
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".