Overcoming technical constraints for obtaining sustainable development with open source appropriate technology
Bibliographic record
Abstract
Open source appropriate technology (OSAT) refers to technologies that provide for sustainable development while being designed in the same fashion as free and open-source software. Facilitated by advances in information technology software and hardware, new ways to disseminate information such as wikis and Internet-enabled mobile phones, the global development of OSAT has emerged as a reality. This paper shows the sharing of design processes, appropriate tools, and technical information is enables more effective and rapid development of appropriate technologies for both industrialized and nonindustrialized regions. This sharing will require the appropriate technology community to adopt open standards/licenses, document knowledge, and build on previous work. This paper offers solutions in the form of both business models and tools to overcome technical constraints of OSAT development in the forms of the platforms necessary on which to share and build knowledge about appropriate technologies. These solutions are open, easily accessible for those in need, have a low barrier to entry for both users and information creators, and must be vetted in order to utilized as a trustworthy source on critical information needs. Current progress towards implementing these solutions will be reviewed and recommendations will be made to further increase the rate of OSAT development.
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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 | 0.043 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".