The Internet and other ICTs as tools and catalysts for sustainable development: innovation for 21st century
Bibliographic record
Abstract
Among the many issues facing sustainable technological development, dispersed and un-coordinated research, as well as poor communication and collaboration of technological knowledge to those that need it, play a major role in many failed and delayed projects. However, the Internet and its multifaceted platforms provide a potential solution to these problems. This paper examines the ways in which the Internet and other information and communication technologies (ICTs) can improve critical shortcomings to sustainable development efforts. Specifically, the strategies of open source appropriate technology (OSAT) and innovation through collaboration are reviewed. The methods are examined by which OSAT is i) accelerating innovation by building and sharing knowledge, ii) improving collaboration and iii) increasing both the efficiency and effectiveness of many programs and projects. Four OSAT case studies are presented for organizations at the forefront of open collaborative technologies for sustainable development: i) Practical Action, ii) Kopernik, iii) Engineering for Change, and iv) The Appropedia Foundation. The key features, models and the approaches employed by these organizations are discussed and conclusions are drawn from these case studies to contribute insight and better guide the development of this new innovation strategy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".