Towards a Scalable Architecture for Smart Villages: The Discovery Phase
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it