Community Building and Information and Communications Technologies: Current Knowledge COMMUNITY BUILDING AND INFORMATION AND COMMUNICATIONS TECHNOLOGIES: CURRENT KNOWLEDGE.
Bibliographic record
Abstract
The study of the impact of information and community technologies (ICTs) on community building has matured in recent years. Though the ‘digital divide’ remains, ICT availability has improved considerably in Australia, Canada, and the USA. Not only do a wider range of people have access to Internet technology, its use is a normal feature in people’s lives. It is now possible to investigate its effect not just on individuals, but also on their communities, in the field of study called ‘community informatics.’ Evidence suggests that ICTs have a positive effect on the tendency of people to join groups, and that many relationships formed in cyberspace continue in physical space. The social capital literature tends to support the proposition that ICTs make a positive contribution to social relationships, though it is possible that social capital is a prerequisite for significant ICT contribution to community life, rather than (or in addition to) a product of this contribution. Traditional community development literature has always emphasized the necessity of community input into local projects for them to be sustainable. The ICT literature now acknowledges that same point: ICTs projects must meet communally identified goals to be successful. Wired communities are most successful when innovation comes from the grassroots up. To this end, ‘soft technology, ’ a people-based technology which includes consultation, training, mutual support, and network building, is an essential partner to the hard technology itself. A paper prepared for the Australian Electronic Governance Conference. Centre for Public
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".