Bibliographic record
Abstract
Introduction: Artificial intelligence (AI) impacts rural dynamics, but its bibliometric study is limited. This paper analyzes academic production on AI and its socioeconomic impact in rural areas between 2019 and 2022. Methodology: A search was conducted in Scopus, Web of Science, and other databases using the terms "AI," "socioeconomic transformations," and "rural." The data was processed using Bibliometrix and VOSviewer to analyze productivity, collaboration networks, and keyword co-occurrence. Duplicates were removed, and filters were applied by year, document type, and thematic relevance. Results: A large number of relevant publications were identified, with an annual growth of a quarter. Thematic core topics included smart agriculture, the digital divide, and rural employment. The United States, China, and India led the scientific production. Conclusions: AI is emerging as an expanding field for rural development, but inequalities in access persist. Further studies on public policy and inclusion are needed.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".