Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2503.05950

From Community Network to Community Data: Towards Combining Data Pool and Data Cooperative for Data Justice in Rural Areas

Jean Louis Fendji Kedieng Ebongue
View original source ↗Read source document ↗Export metadata ↓

Abstract

This study explores the shift from community networks (CNs) to community data in rural areas, focusing on combining data pools and data cooperatives to achieve data justice and foster and a just AI ecosystem. With 2.7 billion people still offline, especially in the Global South, addressing data justice is critical. While discussions related to data justice have evolved to include economic dimensions, rural areas still struggle with the challenge of being adequately represented in the datasets. This study investigates a Community Data Model (CDM) that integrates the simplicity of data pools with the structured organization of data cooperatives to generate local data for AI for good. CDM leverages CNs, which have proven effective in promoting digital inclusion, to establish a centralized data repository, ensuring accessibility through open data principles. The model emphasizes community needs, prioritizing local knowledge, education, and traditional practices, with an iterative approach starting from pilot projects. Capacity building is a core component of digital literacy training and partnership with educational institutions and NGOs. The legal and regulatory dimension ensures compliance with data privacy laws. By empowering rural communities to control and manage their data, the CDM fosters equitable access and participation and sustains local identity and knowledge. This approach can mitigate the challenges of data creation in rural areas and enhance data justice. CDM can contribute to AI by improving data quality and relevance, enabling rural areas to benefit from AI advancements.

Explore related subjects

cs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jean Louis Fendji Kedieng Ebongue. 2025-03-07. From Community Network to Community Data: Towards Combining Data Pool and Data Cooperative for Data Justice in Rural Areas. https://arxiv.org/abs/2503.05950

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Printed but not benchmarkable: most building-decarbonisation disclosure cannot be matched to the pathways that stranding regulation assumes

Cities are beginning to enforce carbon limits on existing buildings. Science-based decarbonisation pathways set those limits one asset type and one jurisdiction at a time. Owners, however, report for the whole firm. We measure what that mismatch costs on two sets of public corporate reports: a census of 502 reports from the 119 listed built-environment firms with a collected report inside a 2,246-firm panel (2003-2023), and 519 real-estate reports from 101 firms (2007-2024). BeDA, a multimodal language-model tool whose reliability we test first, read them. Running the pathway frameworks' own entry tests over published disclosure: 16.5% of census reports (43.7% of real-estate reports) print an operational carbon intensity per square metre; 6.6% (25.0%) can be matched to a pathway for their property type in a covered jurisdiction; and only 5.0% (16.4%) disclose the floor area they divided by. Of the failures at the pathway test, 82-84% follow from reports lumping the portfolio together and 16-18% from a missing curve in the pathway library. The obstacle is the reporting unit, not missing data. The rate is roughly twice as high for European as for US listings (65-71% versus 35% in listed real estate). We also show that a US portfolio's carbon verdict cannot be worked out from disclosure at all. Within one climate zone, the pathway's carbon limit varies by up to 2.79-fold with the electricity subregion, which no report names; its energy limit does not move. Extraction is checked against the source PDFs (97.3% of extracted intensities appear verbatim) and repeats on a second extractor (kappa = 0.97). Recall of the non-disclosing class was 95.1% in a blinded hand audit of 122 reports. The fix follows from the measurement: split intensity by asset type and jurisdiction, and report floor area.

cs.CY↗
arXiv2026

Incipit: Axiom-Grounded Scaffolding for Human-AI Literary Creation

Large language models can produce fluent prose from short prompts, but a direct interaction gives writers little access to the assumptions that shape a long narrative. We present Incipit, an implemented research prototype that inserts an explicit planning layer between writer intent and generated prose. The layer is grounded in literary axioms, which are curated and reusable propositions about human experience and narrative craft. The prototype connects a knowledge base of 1455 axioms and 472 typed relationships to a five round direction dialogue, a retrieval and selection pipeline, and a three level blueprint covering creative premises, story beats, character arcs, and chapter outlines. Writers can inspect and edit the resulting structures before using them as context for scene generation. Additional modules support real event abstraction and five dimensional diagnostic feedback. We describe the design rationale, data flow, implementation boundaries, and a worked design example. Because no controlled user study or independently rated output study has yet been completed, we do not claim that the system improves literary quality. Instead, we outline a future preregistered comparison designed to distinguish the contribution of axiom grounding from that of hierarchical planning. The paper contributes a concrete architecture for making literary knowledge an inspectable coordination object in human AI writing.

cs.CY↗
arXiv2026

Automating Constructive Assessment with Large Language Models: Toward Scalable and Repeated Evaluation of Practical Competence

This study aimed to automate hierarchical diagnostic reasoning (HDR), a constructive method for evaluating practical judgment skills, by developing and testing an evaluation process using a large language model. HDR is a descriptive task that measures higher-order cognitive skills by requiring students to identify and explain errors in case-study-based problems. However, it requires expertise and effort to develop and evaluate. Hence, we proposed and empirically validated the automatic (1) generation of case problems containing errors aligned with educational intentions, (2) scoring of descriptive answers, and (3) generation of structured feedback based on incorrect answers, achieved solely through prompt design without fine-tuning. The internal consistency and construct validity of the generated problems were supported by the experimental score distribution and Cronbach's alpha (0.78). The agreement between automated and human ratings reached 100% under some conditions. The feedback was rated as being as convincing and useful as that from human instructors, demonstrating a practical framework for implementing HDR-based constructive assessment with reproducibility, immediacy, and low cost. The flexibility of large language models will also enable repeated and longitudinal assessments while maintaining structure, showing broad potential for application in educational settings.

cs.CY↗