Bibliographic record
Abstract
This paper explores the contribution that information and communication technologies (ICTs) make to regional development. ICTs offer the potential to improve the quality of information flow within regional settings. This research investigates the impact that ICTs have at the regional level, and the role they play in developing local, regional and global networks. The ICT networks are themselves affected by local regional culture and this research examines the recursive relationship between the soft networks created by social capital and hard ICT based networks. The setting for this research is regional New Zealand. One urban and one rural region have been studied over the twenty year period, from 1985 to 2005. In the regional setting tacit or soft knowledge is built up through networks such as “Women in Dairying” and “Grey Power”. Social interaction and the exchange of information are easier and cheaper in the regional context. Within the regional setting these soft social networks interact with hard ICT based networks, and when brought together they can facilitate both national and international information flows. A historical approach has been used to plot the development of both soft and hard networks in the two contrasting regions. In terms of soft networks New Zealand became more culturally diverse and liberal especially in the urban areas. Social capital in terms of volunteering and membership of community groups was high throughout the period though its form changed. As the country worked on developing new global trading links with new partners in Asia, there was a parallel fast take up of new ICTs such as the Internet which offered the opportunity to overcome some of the barriers created by the geographic remoteness of New Zealand. The focus of this paper will be on the interplay between these soft social networks and the hard ICT based networks and the role they play in establishing national and global linkages.
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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 | 0.006 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".