Bibliographic record
Abstract
Being able to connect high-speed computing and other information technologies into broadband communication networks presents local communities with some of their best chances for renewal. Such technologies are now widely perceived to be not just a nice amenity among corporations and such non-profit organizations as universities but a social and economic necessity for communities struggling to find their place in a rapidly changing world. Today, citizens want and expect their local communities to be “wired” for broadband digital transactions, whether for family, business, education or leisure. Such networks have become a necessity for attracting and retaining the new “knowledge workforce” that will be key to transforming communities into digital societies where people will want to live and work. Since the Internet is a global phenomenon, some of the challenges of globalization for local communities and regions are introduced in this article and suggestions for turning those challenges into opportunities are offered. To attain maximum benefit from the new wired and wireless networks, local strategies must be developed for its implementation and applications must be chosen with some sensitivity to local needs. New Growth theory is used to show why communities must plan their development agenda, and case studies of the Intelligent Community Forum are included to show how strategically used ICTs are allowing local communities to be contributors in global markets.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.444 | 0.445 |
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".