An international comparison of computer networks’ use and potential use
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
This paper explores nursing organizations’ experiences, views and policies on computer networks; their ability to access such networks; and to what uses nursing organizations can put the Internet and other networks. There were significant differences between poor and rich countries with respect to access to networking facilities, but not with respect to opinions of the use or potential use of networking. Roughly two-fifths of nursing organizations had access to either the Internet or a local area network (LAN). Richer countries were more likely to have access to both the Internet and LANs. About a half of organizations had email, but only about a quarter accessed email lists. About two-fifths used the World Wide Web (WWW) but only about a tenth had access to USENET newsgroups. There was a significant difference between rich and poor countries with respect to WWW and USENET, with richer countries having greater access. Training for nurses and policies for using computer networks were identified in few organizations, although the potential for computer networks was understood by most. Enthusiasm for using computer networks was particularly noted in poorer and geographically more remote countries. From a list of ten services available via the Internet, the network resource most valued by nursing organizations was online databases; the least valued was videoconferencing.
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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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