Bibliographic record
Abstract
Throughout the history of computing, developments in human-computer interaction (HCI) have often been preceded by breakthroughs in display and input technologies. The first use of a cathode ray tube (CRT) to display computer-generated (radar) data, in the Canadian DATAR [6] and MIT’s Whirlwind projects of the early 1950s, led to the development of the trackball and light pen. That development, in turn, influenced Sutherland’s and Engelbart’s work on interactive computer graphics, the mouse, and the graphical user interface (GUI) during the early 1960s. According to Alan Kay [3], seeing the first liquid crystal display (LCD) had a similar disruptive effect on his thinking about interactivity at Xerox PARC during the early 1970s. His vision of Dynabook led to the development of Smalltalk, the Alto GUI (1973), and eventually, the Tablet PC [2]: “... Another thing that we saw in 1968 was a tiny 1 ” square first flat panel display down at the University of Illinois. We realized it was going to be a matter of years until you could put all the electronics... on the back of a flat panel display, which I later came to call the Dynabook.”
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.045 |
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".