Harnessing the potential of AI and data analytics for infonomics in developing countries: an architectural model and framework
Bibliographic record
Abstract
Purpose Infonomics applies economic principles to manage and use information as an economic asset. In today’s data-driven economy, information assets significantly contribute to economic growth. However, developing countries face challenges in realizing infonomics, primarily due to weak data infrastructure, privacy concerns and high technology costs. In light of these challenges, this study aims to present an architectural framework to optimize information assets for economic development. Design/methodology/approach This study uses a comprehensive literature review to identify best practices, practical insights and technological trends relevant to infonomics. The insights gained have informed the proposal of an architectural model and framework for implementing infonomics within artificial intelligence (AI) and a data-driven economy, particularly in the context of developing countries. Findings This research proposes an architectural model and framework for the efficient utilization of AI, data analytics and information assets within the field of infonomics to foster economic development in developing countries. The findings provide insights that enable these nations to strategically navigate the information landscape, thereby unlocking the latent value of information and advancing their progress toward sustainable development. Research limitations/implications The proposed model and framework emphasize the potential of infonomics in fostering economic growth. It also offers a systematic approach for developing countries to navigate the information landscape effectively. In this context, the findings clarify that the previously untapped value of information can lead to achievable outcomes and serve as pathways for sustainable growth. Originality/value To the best of the authors’ knowledge, this study is a crucial first step in unlocking the potential of AI, data analytics and information assets as drivers of innovation, competitive advantage and economic prosperity in developing countries.
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.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".