Towards a Scalable Architecture for Smart Villages: The Discovery Phase
Bibliographic record
Abstract
Alleviating poverty, reducing inequality, and achieving economic prosperity and well-beingis a global challenge. The spread and quantum of this daunting challenge calls for a scalable solution.The aim of the ‘Scalable Architecture for Smart Villages’ project is to contribute to an eective solutionwhich addresses scale as well as customization. In order to achieve both in our new framework forsmart villages, we take an endogenous approach. This approach emphasizes learning which will createa catalytic eect for scale. Learning is an essential component in the process, both for the researchersas well as members of the community. With these principles in mind, our approach proceeds in fourphases, namely discovery, planning, resourcing and executing. In this paper we outline the discoveryphase, which will lay the foundation for developing our framework of scalable smart villages.The Discovery Phase is a research process where the community learns about itself and the researcherslearn about the underlying factors that can help uplift and develop a smart village. Using conventionalqualitative and quantitative research methodology, the researchers and the community will generatebaseline data which will help calibrate villages for future development into smart villages.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".