Recent Topical Research on Global, Energy, Health & Medical, and Tourism Economics, and Global Software: An Overview
Bibliographic record
Abstract
The paper presents an overview of recent topical research on global, energy, health & medical, and tourism economics, and global software. We have interpreted “global†in the title of the Journal of Reviews on Global Economics to cover contributions that have a global impact on economics, thereby making it "global economics". In this sense, the paper is concerned with papers on global, energy, health & medical, and tourism economics, as well as global software algorithms that have global economic impacts. The topics covered include re-opening the Silk Road to transform Chinese trade, education and skill mismatches, code of practice and indicators for quality management of official statistics, projections of energy use and carbon emissions, multi-fuel allocation for power generation using genetic algorithms, optimal active energy loss with feeder routing and renewable energy for smart grid distribution, demand for narcotics with policy implications, computer technology to improve medical information, heritage tourism, ecotourism impacts on the economy, society and environment, taxi drivers' cross-cultural communication problems and challenges, hybrid knowledge discovery system based on items and tags, game development platform to improve advanced programming skills, quadratic approximation of the newsvendor problem with imperfect quality, classification of workflow management systems for emails, academic search engine for personalized rankings, creative and learning processes using game-based activities, personal software process with automatic requirements traceability to support start-ups, and comparing statistical and data mining techniques for enrichment ontology with instances.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".