Social Appropriation of Internet Technology: a South African platform
Bibliographic record
Abstract
The social appropriation of Internet technologies is emerging as a research and practice field called Community Informatics (CI). Various research groups (for example Australia, UK, Canada, Latin America, Italy etc.) are contributing to Government's gradual realisation that the enabling of communities with Internet technologies can boost local economic and social development, as well as enhance personal empowerment. Civil society digital inclusion, linked with World Summit on the Information Society (WSIS), is now seen as a necessary component of social development strategy. However, various attempts at such initiatives have met different forms of resistance and various levels of success. Cape Technikon is establishing a research hub in Cape Town as part of the international CIRN (Community Informatics Research Network). This project will aim to establish a research, teaching and community engagement platform in Community Informatics (the social appropriation of Internet Technologies for local benefit) which will link Cape Technikon into a rapidly expanding international area of research and teaching as well as putting it into a national leadership position. Outputs will include demonstrated linkages with local, national and international Community Informatics efforts, the establishment of local projects and entities, the establishment of courses, the attraction of undergraduate and post graduate students, a profile with national and international funding agencies, publications, funding proposals, internal agency recognition in research and teaching, a program of high profile and internationally recognised visiting research fellows and academic sabbaticals. This paper discusses activities towards this South African initiative and experience elsewhere.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".