Socioeconomic perspectives and effects of Generative AI in the agrifood industry

A Living Literature Review

Introduction

This repository contains the source code for a dynamic web application that serves as a living literature review on the application of Generative AI within the agrifood sector. The project aims to provide an up-to-date, interactive repository of academic papers, forming a component of the doctoral research conducted at the Universitat Politècnica de València.

The application automatically fetches recent publications from leading academic databases, filters them for relevance, and presents them in a user-friendly interface. This facilitates ongoing analysis of the socioeconomic impacts and technological advancements of Generative AI in this critical industry.

Doctoral Research Context

This work is part of the PhD thesis by:

• PhD Candidate: Beatriz Vallina
• Institution: Agrifood Economics, Universitat Politècnica de València

Supervised by:

• Dr Roberto Cervelló
• Dr Juan José Llul

Features

• Real-time Data Fetching: Automatically retrieves paper data from the Semantic Scholar and arXiv APIs.
• Dynamic Filtering: Displays only recent publications (from January 2025 onwards) to maintain the relevance of the review.
• Interactive Interface: Users can click on any paper to view its abstract, authors, and other metadata in a modal window.
• DOI Integration: Provides direct links to the papers via their Digital Object Identifier (DOI) where available.
• Literature Map: Includes an interactive literature map generated with Litmaps to visualise connections between publications.

Technical Overview

The application is built with standard web technologies:

• HTML5
• CSS3
• Vanilla JavaScript

It fetches data asynchronously and dynamically generates the content on the client-side.

Local Development

To run this project locally, a simple web server is required to handle API requests correctly due to browser security policies (CORS).

1. Ensure you have Python installed.

2. Navigate to the project's root directory in your terminal.

3. Start the local server with the following command:

   # For Python 3
   python -m http.server

4. Open your web browser and navigate to http://localhost:8000.

Deployment

This project is designed for static hosting platforms and can be easily deployed using services like GitHub Pages.

Data Sources

This living review is made possible by the public APIs provided by:

• Semantic Scholar
• arXiv