Creating Applications and a Culture of Using
Bibliographic record
Abstract
Chapters I to IV have introduced the networked community and described its environment in terms of the Network Society, technology, telecommunication regulations and public policy, and the knowledge workforce. In this chapter, the focus shifts to the content specifics—the telecommunication and software applications found on the broadband networks. The usefulness of these applications can stimulate the creativity of users, leading to a continuum of use, otherwise known as a “culture of use”. The difficulty in benchmarking innovative applications is that they change minute by minute; what is exciting today will probably be common tomorrow. Nevertheless, even established network applications should be considered because they represent innovations that might serve as springboards to next-generation production, making communities more distinctive, competitive, and creative. Several types of worldwide community innovations in applications are described here. This chapter will deal with: • A description of applications and groupings of applications; • An overview of sector-specific applications and some international examples; • A discussion on technology adoption issues that should be considered in developing a culture of use; • Measurement and evaluation approaches.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".