Bibliographic record
Abstract
The papers in this Special Issue were selected for development from those presented at the second ACM SIGCHI/SIGCAPH conference on Computers and Universal Usability, CUU 2003, held in Vancouver in November 2003. and follows the first Special Issue on Universal Usability (Interacting with Computers 14, 2002). In the early days of computers, the concept of ‘universal access’ would have been meaningless. Computers were few in number, filled air-conditioned rooms and required very special skills and knowledge to operate. The range of applications was correspondingly limited—they might be used to calculated the trajectory of artillery shells or to break secrete ciphers, but they were capable of nothing that would be of any interest to the average person. The first significant change came, of course, with the advent of the personal computer, the PC. The PC was different in many ways. It was small, so that it could be used in an ordinary room. Most likely that room was an office, because although the PC was very much cheaper than its mainframe ancestor, it still cost more than the average person would want to spend. Indeed, they would not want to spend that much because they would see little benefit from owning a computer; the things they could do with it (applications they could run) were limited, and generally orientated to business requirements. There was a persistent force driving the PC market, though: the more PCs were sold, the greater numbers were manufactured and the more were built the cheaper they became. As they became cheaper there was a need to sell them, to maintain the momentum. So manufacturers had to find and to create new markets.
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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.030 | 0.042 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".