Bibliographic record
Abstract
This chapter focuses on knowledge workers—who they are and what they do, and the impact they have on organisations and communities in the Network Society. As technology-savvy individuals, they have the training to understand and apply telecommunications and electronic media at work, at home and in the community. Because of their ICT skills and potential contributions to innovation and productivity, knowledge workers constitute a critical labour market for networked communities. Training and education institutions can play an important role in ensuring the local supply of ICT skills. To illustrate these points, four networked communities are described: • Issy-les-Moulineaux, France. This suburb of Paris has transformed itself into a preferred location for knowledge workers to live and work; • Mitaka, Japan. Mitaka is a suburb of Tokyo offering exceptional quality of life to its knowledge workers; • Taipei, Taiwan. This is a large city with a CyberCity Plan and an impressive labour force; • Waterloo, Ontario, Canada. This university town has developed an international reputation based on public-private collaboration and entrepreneurship. The chapter ends with suggestions for the measurement and evaluation of a community’s knowledge workforce.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.011 |
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 teacher head, 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".